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lead/tsp_data/tsp_test.ipynb
yangyudong bffda01a6d 修改绘图
2025-05-15 22:00:38 +08:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['pr439.tsp', 'pla7397.tsp', 'gr96.tsp', 'rd100.tsp', 'swiss42.tsp', 'rl5934.tsp', 'pcb442.tsp', 'u2319.tsp', 'gil262.tsp', 'pcb3038.tsp', 'brazil58.tsp', 'lin105.tsp', 'brg180.tsp', 'fl417.tsp', 'tsp225.tsp', 'si1032.tsp', 'fl1400.tsp', 'nrw1379.tsp', 'd2103.tsp', 'kroA150.tsp', 'pcb1173.tsp', 'd198.tsp', 'fl1577.tsp', 'gr666.tsp', 'ch130.tsp', 'kroB100.tsp', 'bays29.tsp', 'u1060.tsp', 'berlin52.tsp', 'eil51.tsp', 'rl1304.tsp', 'u2152.tsp', 'si535.tsp', 'ulysses16.tsp', 'gr21.tsp', 'u724.tsp', 'gr24.tsp', 'kroD100.tsp', 'linhp318.tsp', 'pr299.tsp', 'rd400.tsp', 'gr202.tsp', 'vm1084.tsp', 'rat575.tsp', 'd1655.tsp', 'ch150.tsp', 'd15112.tsp', 'pr107.tsp', 'kroB200.tsp', 'dantzig42.tsp', 'hk48.tsp', 'brd14051.tsp', 'a280.tsp', 'd1291.tsp', 'gr229.tsp', 'pr264.tsp', 'pr76.tsp', 'gr17.tsp', 'd493.tsp', 'dsj1000.tsp', 'pr136.tsp', 'rat195.tsp', 'rl11849.tsp', 'att532.tsp', 'kroA100.tsp', 'fri26.tsp', 'pla85900.tsp', 'ali535.tsp', 'ulysses22.tsp', 'kroB150.tsp', 'bier127.tsp', 'kroC100.tsp', 'usa13509.tsp', 'eil76.tsp', 'pr124.tsp', 'rl1323.tsp', 'p654.tsp', 'gr431.tsp', 'rl1889.tsp', 'd657.tsp', 'eil101.tsp', 'fnl4461.tsp', 'pr2392.tsp', 'rat783.tsp', 'ts225.tsp', 'u1432.tsp', 'gr48.tsp', 'u1817.tsp', 'lin318.tsp', 'd18512.tsp', 'rl5915.tsp', 'att48.tsp', 'st70.tsp', 'rat99.tsp', 'fl3795.tsp', 'burma14.tsp', 'u159.tsp', 'pla33810.tsp', 'kroA200.tsp', 'u574.tsp', 'pr1002.tsp', 'pr152.tsp', 'gr137.tsp', 'pa561.tsp', 'gr120.tsp', 'si175.tsp', 'bayg29.tsp', 'pr226.tsp', 'vm1748.tsp', 'pr144.tsp', 'kroE100.tsp']\n"
]
}
],
"source": [
"import os\n",
"import numpy as np\n",
"import time\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from tsp_algo import *\n",
"\n",
"# 获取tsp_data目录下所有tsp文件\n",
"data_dir = \"../\"\n",
"data_dir2 = \"../tsp_data/data/tsplib/\"\n",
"test_files = [f for f in os.listdir(data_dir2) if f.endswith('tsp')]\n",
"print(test_files)\n",
"\n",
"# 定义评估函数\n",
"def evaluate_tsp(tsp_func, distances):\n",
" \"\"\"评估TSP算法的性能\"\"\"\n",
" try:\n",
" # 计时并执行TSP\n",
" start_time = time.time()\n",
" path = tsp_func(distances)\n",
" end_time = time.time()\n",
" execution_time = end_time - start_time\n",
" \n",
" # 计算路径长度\n",
" total_distance = 0\n",
" n = len(distances)\n",
" for i in range(n):\n",
" total_distance += distances[path[i]][path[(i+1)%n]]\n",
" \n",
" print(f\"执行时间:{execution_time:.4f}秒, 路径长度:{total_distance:.2f}\")\n",
" return total_distance, execution_time\n",
" \n",
" except Exception as e:\n",
" print(f\"评估过程出错: {str(e)}\")\n",
" return float('inf'), float('inf')\n",
"\n",
"# 定义数据加载函数 \n",
"def load_tsp_data(filename):\n",
" \"\"\"读取TSP问题数据\"\"\"\n",
" try:\n",
" cities = []\n",
" filepath = os.path.join(data_dir2, filename)\n",
" with open(filepath, 'r') as f:\n",
" reading_coords = False\n",
" for line in f:\n",
" if line.strip() == \"NODE_COORD_SECTION\":\n",
" reading_coords = True\n",
" continue\n",
" if line.strip() == \"EOF\":\n",
" break\n",
" if reading_coords:\n",
" parts = line.strip().split()\n",
" if len(parts) == 3:\n",
" x, y = float(parts[1]), float(parts[2])\n",
" cities.append((x, y))\n",
" \n",
" # 计算距离矩阵\n",
" n = len(cities)\n",
" distances = np.zeros((n, n))\n",
" for i in range(n):\n",
" for j in range(n):\n",
" if i != j:\n",
" distances[i,j] = np.sqrt((cities[i][0] - cities[j][0])**2 + \n",
" (cities[i][1] - cities[j][1])**2)\n",
" \n",
" return distances\n",
" \n",
" except Exception as e:\n",
" print(f\"读取数据出错: {str(e)}\")\n",
" return None"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"过滤后的文件列表:\n",
"burma14.tsp: 14\n",
"ulysses16.tsp: 16\n",
"ulysses22.tsp: 22\n",
"dantzig42.tsp: 42\n",
"att48.tsp: 48\n",
"eil51.tsp: 51\n",
"berlin52.tsp: 52\n",
"st70.tsp: 70\n",
"pr76.tsp: 76\n",
"eil76.tsp: 76\n",
"gr96.tsp: 96\n",
"rat99.tsp: 99\n",
"rd100.tsp: 100\n",
"kroB100.tsp: 100\n",
"kroD100.tsp: 100\n",
"kroA100.tsp: 100\n",
"kroC100.tsp: 100\n",
"kroE100.tsp: 100\n",
"eil101.tsp: 101\n",
"lin105.tsp: 105\n",
"pr107.tsp: 107\n",
"pr124.tsp: 124\n",
"bier127.tsp: 127\n",
"ch130.tsp: 130\n",
"pr136.tsp: 136\n",
"gr137.tsp: 137\n",
"pr144.tsp: 144\n",
"kroA150.tsp: 150\n",
"ch150.tsp: 150\n",
"kroB150.tsp: 150\n",
"pr152.tsp: 152\n",
"u159.tsp: 159\n",
"rat195.tsp: 195\n",
"d198.tsp: 198\n",
"kroB200.tsp: 200\n",
"kroA200.tsp: 200\n",
"gr202.tsp: 202\n",
"tsp225.tsp: 225\n",
"ts225.tsp: 225\n",
"pr226.tsp: 226\n",
"gr229.tsp: 229\n",
"gil262.tsp: 262\n",
"pr264.tsp: 264\n",
"a280.tsp: 280\n",
"pr299.tsp: 299\n",
"linhp318.tsp: 318\n",
"lin318.tsp: 318\n",
"rd400.tsp: 400\n",
"fl417.tsp: 417\n",
"gr431.tsp: 431\n",
"pr439.tsp: 439\n",
"pcb442.tsp: 442\n",
"d493.tsp: 493\n",
"att532.tsp: 532\n",
"ali535.tsp: 535\n",
"pa561.tsp: 561\n",
"u574.tsp: 574\n",
"rat575.tsp: 575\n",
"p654.tsp: 654\n",
"d657.tsp: 657\n",
"gr666.tsp: 666\n",
"u724.tsp: 724\n",
"rat783.tsp: 783\n",
"dsj1000.tsp: 1000\n",
"pr1002.tsp: 1002\n",
"u1060.tsp: 1060\n",
"vm1084.tsp: 1084\n",
"pcb1173.tsp: 1173\n",
"d1291.tsp: 1291\n",
"rl1304.tsp: 1304\n",
"rl1323.tsp: 1323\n",
"nrw1379.tsp: 1379\n",
"fl1400.tsp: 1400\n",
"u1432.tsp: 1432\n"
]
}
],
"source": [
"def get_file_number(filename):\n",
" \"\"\"从文件名中提取数字部分\"\"\"\n",
" try:\n",
" number = int(''.join(filter(str.isdigit, filename)))\n",
" return number\n",
" except ValueError:\n",
" print(f\"无法从文件名提取数字: {filename}\")\n",
" return float('inf')\n",
"\n",
"def check_file_content(filename):\n",
" \"\"\"检查文件内容是否包含EDGE_WEIGHT_TYPE: EXPLICIT\"\"\"\n",
" try:\n",
" with open(os.path.join(data_dir2, filename), 'r') as f:\n",
" content = f.read()\n",
" return 'EDGE_WEIGHT_TYPE: EXPLICIT' not in content\n",
" except Exception as e:\n",
" print(f\"读取文件出错 {filename}: {str(e)}\")\n",
" return False\n",
"\n",
"# 过滤文件列表\n",
"filtered_files = []\n",
"for f in test_files:\n",
" if get_file_number(f) < 1500 and check_file_content(f):\n",
" filtered_files.append(f)\n",
"\n",
"# 按数字大小排序\n",
"filtered_files.sort(key=get_file_number)\n",
"\n",
"# 更新test_files\n",
"test_files = filtered_files\n",
"\n",
"print(\"过滤后的文件列表:\")\n",
"for file in test_files:\n",
" print(f\"{file}: {get_file_number(file)}\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"测试实例: burma14.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0000秒, 路径长度:38.69\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0000秒, 路径长度:38.69\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.0004秒, 路径长度:32.44\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.0120秒, 路径长度:38.69\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.0206秒, 路径长度:31.23\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0013秒, 路径长度:38.80\n",
"\n",
"测试实例: ulysses16.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0000秒, 路径长度:104.73\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0000秒, 路径长度:104.73\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.0007秒, 路径长度:79.39\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.0099秒, 路径长度:76.87\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.0241秒, 路径长度:73.99\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0009秒, 路径长度:104.65\n",
"\n",
"测试实例: ulysses22.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0000秒, 路径长度:89.64\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0001秒, 路径长度:89.64\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.0010秒, 路径长度:76.99\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.0160秒, 路径长度:89.64\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.0349秒, 路径长度:76.89\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0012秒, 路径长度:91.92\n",
"\n",
"测试实例: dantzig42.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0000秒, 路径长度:0.00\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0000秒, 路径长度:0.00\n",
"\n",
"使用算法: 插入法\n",
"评估过程出错: index 0 is out of bounds for axis 0 with size 0\n",
"\n",
"使用算法: EoH-TSP\n",
"评估过程出错: Sample larger than population or is negative\n",
"\n",
"使用算法: AAD-TSP\n",
"评估过程出错: index 0 is out of bounds for axis 0 with size 0\n",
"\n",
"使用算法: MEoH-TSP\n",
"评估过程出错: index 0 is out of bounds for axis 0 with size 0\n",
"\n",
"测试实例: att48.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0003秒, 路径长度:40526.42\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0002秒, 路径长度:40526.42\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.0124秒, 路径长度:37314.09\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.0307秒, 路径长度:40526.42\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.0671秒, 路径长度:35738.05\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0029秒, 路径长度:37686.87\n",
"\n",
"测试实例: eil51.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0005秒, 路径长度:513.61\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0004秒, 路径长度:513.61\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.0146秒, 路径长度:496.25\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.0342秒, 路径长度:513.61\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.0815秒, 路径长度:465.91\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0030秒, 路径长度:458.95\n",
"\n",
"测试实例: berlin52.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0004秒, 路径长度:8980.92\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0002秒, 路径长度:8980.92\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.0169秒, 路径长度:9014.89\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.0442秒, 路径长度:8980.92\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.0888秒, 路径长度:8178.40\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0040秒, 路径长度:8835.06\n",
"\n",
"测试实例: st70.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0006秒, 路径长度:805.53\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0006秒, 路径长度:805.53\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.0399秒, 路径长度:778.99\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.0584秒, 路径长度:805.53\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.1202秒, 路径长度:753.15\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0047秒, 路径长度:871.65\n",
"\n",
"测试实例: pr76.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0008秒, 路径长度:153461.92\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0007秒, 路径长度:153461.92\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.0511秒, 路径长度:125936.21\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.0688秒, 路径长度:140565.41\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.1371秒, 路径长度:117831.22\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0046秒, 路径长度:123787.14\n",
"\n",
"测试实例: eil76.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0009秒, 路径长度:711.99\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0007秒, 路径长度:711.99\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.0430秒, 路径长度:612.39\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.0673秒, 路径长度:669.24\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.1221秒, 路径长度:622.71\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0046秒, 路径长度:577.27\n",
"\n",
"测试实例: gr96.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0009秒, 路径长度:707.09\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0011秒, 路径长度:707.09\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.0923秒, 路径长度:651.44\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.0769秒, 路径长度:707.09\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.2633秒, 路径长度:623.53\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0065秒, 路径长度:573.48\n",
"\n",
"测试实例: rat99.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0013秒, 路径长度:1564.72\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0011秒, 路径长度:1564.72\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.1136秒, 路径长度:1482.02\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.1031秒, 路径长度:1564.72\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.1965秒, 路径长度:1377.07\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0057秒, 路径长度:1492.74\n",
"\n",
"测试实例: rd100.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0005秒, 路径长度:9941.16\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0012秒, 路径长度:9941.16\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.1072秒, 路径长度:8979.37\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.0986秒, 路径长度:9941.16\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.2223秒, 路径长度:8864.57\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0058秒, 路径长度:10510.18\n",
"\n",
"测试实例: kroB100.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0015秒, 路径长度:29155.04\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0013秒, 路径长度:29155.04\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.1206秒, 路径长度:25580.92\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.0981秒, 路径长度:29155.04\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.2087秒, 路径长度:25392.46\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0033秒, 路径长度:25679.71\n",
"\n",
"测试实例: kroD100.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0012秒, 路径长度:26950.46\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0007秒, 路径长度:26950.46\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.1119秒, 路径长度:25204.27\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.0862秒, 路径长度:26950.46\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.1775秒, 路径长度:24565.25\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0061秒, 路径长度:26072.81\n",
"\n",
"测试实例: kroA100.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0015秒, 路径长度:26856.39\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0010秒, 路径长度:26856.39\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.0981秒, 路径长度:24307.78\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.0825秒, 路径长度:26856.39\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.1498秒, 路径长度:22957.15\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0061秒, 路径长度:22683.29\n",
"\n",
"测试实例: kroC100.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0012秒, 路径长度:26327.36\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0015秒, 路径长度:26327.36\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.0983秒, 路径长度:25262.17\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.0842秒, 路径长度:26327.36\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.1798秒, 路径长度:23253.25\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0049秒, 路径长度:24294.06\n",
"\n",
"测试实例: kroE100.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0011秒, 路径长度:27587.19\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0008秒, 路径长度:27587.19\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.1026秒, 路径长度:25902.00\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.0972秒, 路径长度:27587.19\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.1961秒, 路径长度:24382.95\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0061秒, 路径长度:25221.45\n",
"\n",
"测试实例: eil101.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0011秒, 路径长度:825.24\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0013秒, 路径长度:825.24\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.1086秒, 路径长度:702.96\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.0867秒, 路径长度:847.59\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.2088秒, 路径长度:702.70\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0051秒, 路径长度:720.41\n",
"\n",
"测试实例: lin105.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0014秒, 路径长度:20362.76\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0012秒, 路径长度:20362.76\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.1155秒, 路径长度:16934.62\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.0962秒, 路径长度:20362.76\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.1928秒, 路径长度:18371.79\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0026秒, 路径长度:19041.58\n",
"\n",
"测试实例: pr107.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0016秒, 路径长度:46678.15\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0014秒, 路径长度:46678.15\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.1403秒, 路径长度:52587.76\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.1044秒, 路径长度:47029.63\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.1547秒, 路径长度:44905.53\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0060秒, 路径长度:51115.61\n",
"\n",
"测试实例: pr124.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0017秒, 路径长度:69299.43\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0015秒, 路径长度:69299.43\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.2034秒, 路径长度:65318.19\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.1309秒, 路径长度:69299.43\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.1921秒, 路径长度:63601.53\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0054秒, 路径长度:68371.30\n",
"\n",
"测试实例: bier127.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0015秒, 路径长度:135751.78\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0016秒, 路径长度:135751.78\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.2007秒, 路径长度:140690.94\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.1216秒, 路径长度:135751.78\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.2196秒, 路径长度:123202.55\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0069秒, 路径长度:138054.23\n",
"\n",
"测试实例: ch130.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0017秒, 路径长度:7575.29\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0020秒, 路径长度:7575.29\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.2338秒, 路径长度:7279.21\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.1343秒, 路径长度:7575.29\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.2306秒, 路径长度:7093.18\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0070秒, 路径长度:7091.76\n",
"\n",
"测试实例: pr136.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0020秒, 路径长度:120777.86\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0014秒, 路径长度:120777.86\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.2884秒, 路径长度:109587.25\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.1478秒, 路径长度:118776.81\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.2450秒, 路径长度:110347.07\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0074秒, 路径长度:123247.21\n",
"\n",
"测试实例: gr137.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0024秒, 路径长度:1022.22\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0017秒, 路径长度:1022.22\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.2412秒, 路径长度:821.29\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.1438秒, 路径长度:1022.22\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.5256秒, 路径长度:849.98\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0085秒, 路径长度:901.99\n",
"\n",
"测试实例: pr144.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0025秒, 路径长度:61650.72\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0021秒, 路径长度:61650.72\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.3768秒, 路径长度:73033.13\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.1484秒, 路径长度:61650.72\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.2336秒, 路径长度:61214.65\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0070秒, 路径长度:60133.16\n",
"\n",
"测试实例: kroA150.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0025秒, 路径长度:33609.87\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0019秒, 路径长度:33609.87\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.3564秒, 路径长度:29966.54\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.1586秒, 路径长度:33609.87\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.4204秒, 路径长度:28767.65\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0089秒, 路径长度:31060.89\n",
"\n",
"测试实例: ch150.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0028秒, 路径长度:8194.61\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0020秒, 路径长度:8194.61\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.3516秒, 路径长度:7994.29\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.1501秒, 路径长度:8194.61\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.3711秒, 路径长度:7161.32\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0087秒, 路径长度:7589.72\n",
"\n",
"测试实例: kroB150.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0020秒, 路径长度:32825.75\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0024秒, 路径长度:32825.75\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.3577秒, 路径长度:31588.68\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.1559秒, 路径长度:32825.75\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.4201秒, 路径长度:28181.08\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0090秒, 路径长度:29848.14\n",
"\n",
"测试实例: pr152.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0025秒, 路径长度:85702.95\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0022秒, 路径长度:85702.95\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.3534秒, 路径长度:88530.82\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.1499秒, 路径长度:85702.95\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.2910秒, 路径长度:80448.65\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0070秒, 路径长度:92682.12\n",
"\n",
"测试实例: u159.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0022秒, 路径长度:54669.03\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0023秒, 路径长度:54669.03\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.3635秒, 路径长度:49981.41\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.1661秒, 路径长度:57436.69\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.3241秒, 路径长度:50416.01\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0079秒, 路径长度:51577.24\n",
"\n",
"测试实例: rat195.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0036秒, 路径长度:2761.96\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0037秒, 路径长度:2761.96\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.8294秒, 路径长度:2814.57\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.2163秒, 路径长度:2761.96\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.5768秒, 路径长度:2490.60\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0116秒, 路径长度:2575.61\n",
"\n",
"测试实例: d198.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0048秒, 路径长度:18620.07\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0040秒, 路径长度:18620.07\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.8395秒, 路径长度:17631.80\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.2329秒, 路径长度:18605.19\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.6631秒, 路径长度:17340.94\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0113秒, 路径长度:19454.99\n",
"\n",
"测试实例: kroB200.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0039秒, 路径长度:36981.59\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0034秒, 路径长度:36981.59\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.8084秒, 路径长度:35421.70\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.2069秒, 路径长度:36981.59\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.7495秒, 路径长度:34372.39\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0099秒, 路径长度:34079.08\n",
"\n",
"测试实例: kroA200.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0038秒, 路径长度:35798.41\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0039秒, 路径长度:35798.41\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.8769秒, 路径长度:35337.51\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.2339秒, 路径长度:35798.41\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.8138秒, 路径长度:30701.99\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0106秒, 路径长度:33224.38\n",
"\n",
"测试实例: gr202.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0033秒, 路径长度:619.40\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0033秒, 路径长度:619.40\n",
"\n",
"使用算法: 插入法\n",
"执行时间:0.8244秒, 路径长度:570.14\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.2186秒, 路径长度:619.40\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.9231秒, 路径长度:529.30\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0095秒, 路径长度:559.57\n",
"\n",
"测试实例: tsp225.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0040秒, 路径长度:4829.00\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0047秒, 路径长度:4829.00\n",
"\n",
"使用算法: 插入法\n",
"执行时间:1.2282秒, 路径长度:4468.20\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.2567秒, 路径长度:4786.42\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:1.2099秒, 路径长度:4169.09\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0128秒, 路径长度:4430.19\n",
"\n",
"测试实例: ts225.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0055秒, 路径长度:152493.55\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0044秒, 路径长度:152493.55\n",
"\n",
"使用算法: 插入法\n",
"执行时间:1.1208秒, 路径长度:160009.16\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.2431秒, 路径长度:146183.10\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.6736秒, 路径长度:139697.02\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0116秒, 路径长度:131162.09\n",
"\n",
"测试实例: pr226.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0047秒, 路径长度:94685.45\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0038秒, 路径长度:94685.45\n",
"\n",
"使用算法: 插入法\n",
"执行时间:1.1522秒, 路径长度:91024.65\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.3356秒, 路径长度:94402.09\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.5864秒, 路径长度:87543.04\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0140秒, 路径长度:96212.20\n",
"\n",
"测试实例: gr229.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0060秒, 路径长度:2014.71\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0057秒, 路径长度:2014.71\n",
"\n",
"使用算法: 插入法\n",
"执行时间:1.2088秒, 路径长度:1825.83\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.2919秒, 路径长度:2014.71\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:1.0688秒, 路径长度:1764.18\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0159秒, 路径长度:1987.56\n",
"\n",
"测试实例: gil262.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0067秒, 路径长度:3241.47\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0070秒, 路径长度:3241.47\n",
"\n",
"使用算法: 插入法\n",
"执行时间:1.8069秒, 路径长度:2804.23\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.3174秒, 路径长度:3259.42\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:1.6125秒, 路径长度:2757.03\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0139秒, 路径长度:2748.90\n",
"\n",
"测试实例: pr264.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0072秒, 路径长度:58022.86\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0068秒, 路径长度:58022.86\n",
"\n",
"使用算法: 插入法\n",
"执行时间:1.9408秒, 路径长度:58225.34\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.3232秒, 路径长度:58328.28\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:0.7822秒, 路径长度:56762.06\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0159秒, 路径长度:59000.73\n",
"\n",
"测试实例: a280.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0087秒, 路径长度:3148.11\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0075秒, 路径长度:3148.11\n",
"\n",
"使用算法: 插入法\n",
"执行时间:2.3356秒, 路径长度:3101.79\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.4066秒, 路径长度:3182.09\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:1.4158秒, 路径长度:2828.71\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0153秒, 路径长度:3448.00\n",
"\n",
"测试实例: pr299.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0083秒, 路径长度:59899.01\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0093秒, 路径长度:59899.01\n",
"\n",
"使用算法: 插入法\n",
"执行时间:2.6661秒, 路径长度:58124.45\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.3982秒, 路径长度:60220.49\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:1.5654秒, 路径长度:52408.65\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0157秒, 路径长度:61338.05\n",
"\n",
"测试实例: linhp318.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0101秒, 路径长度:54033.58\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0078秒, 路径长度:54033.58\n",
"\n",
"使用算法: 插入法\n",
"执行时间:3.1254秒, 路径长度:49454.81\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.4805秒, 路径长度:54033.58\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:2.2263秒, 路径长度:49153.11\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0260秒, 路径长度:50085.92\n",
"\n",
"测试实例: lin318.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0107秒, 路径长度:54033.58\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0097秒, 路径长度:54033.58\n",
"\n",
"使用算法: 插入法\n",
"执行时间:3.2427秒, 路径长度:49454.81\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.4752秒, 路径长度:54033.58\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:2.0872秒, 路径长度:49153.11\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0184秒, 路径长度:50085.92\n",
"\n",
"测试实例: rd400.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0146秒, 路径长度:19168.05\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0142秒, 路径长度:19168.05\n",
"\n",
"使用算法: 插入法\n",
"执行时间:6.5915秒, 路径长度:18629.98\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.6801秒, 路径长度:19168.05\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:4.1070秒, 路径长度:16651.71\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0254秒, 路径长度:17599.52\n",
"\n",
"测试实例: fl417.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0150秒, 路径长度:15114.12\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0148秒, 路径长度:15114.12\n",
"\n",
"使用算法: 插入法\n",
"执行时间:6.8940秒, 路径长度:14179.84\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.7143秒, 路径长度:15256.42\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:7.6544秒, 路径长度:13630.60\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0244秒, 路径长度:13680.66\n",
"\n",
"测试实例: gr431.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0173秒, 路径长度:2516.25\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0172秒, 路径长度:2516.25\n",
"\n",
"使用算法: 插入法\n",
"执行时间:7.8334秒, 路径长度:2214.43\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.7366秒, 路径长度:2516.25\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:5.0698秒, 路径长度:2153.00\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0273秒, 路径长度:2263.78\n",
"\n",
"测试实例: pr439.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0160秒, 路径长度:131282.09\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0185秒, 路径长度:131282.09\n",
"\n",
"使用算法: 插入法\n",
"执行时间:8.5353秒, 路径长度:130067.88\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.7811秒, 路径长度:137778.50\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:4.3660秒, 路径长度:118466.33\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0269秒, 路径长度:134814.09\n",
"\n",
"测试实例: pcb442.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0234秒, 路径长度:61984.05\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0198秒, 路径长度:61984.05\n",
"\n",
"使用算法: 插入法\n",
"执行时间:8.6915秒, 路径长度:60891.83\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.7918秒, 路径长度:61234.77\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:4.3208秒, 路径长度:54291.55\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0248秒, 路径长度:58899.52\n",
"\n",
"测试实例: d493.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0242秒, 路径长度:43646.38\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0226秒, 路径长度:43646.38\n",
"\n",
"使用算法: 插入法\n",
"执行时间:11.6873秒, 路径长度:39982.31\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:0.9321秒, 路径长度:43710.70\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:7.6122秒, 路径长度:38869.88\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0316秒, 路径长度:43050.15\n",
"\n",
"测试实例: att532.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0280秒, 路径长度:112099.45\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0257秒, 路径长度:112099.45\n",
"\n",
"使用算法: 插入法\n",
"执行时间:14.5934秒, 路径长度:102201.61\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:1.0641秒, 路径长度:112099.45\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:9.7291秒, 路径长度:99028.54\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0439秒, 路径长度:103710.35\n",
"\n",
"测试实例: ali535.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0268秒, 路径长度:2671.07\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0272秒, 路径长度:2671.07\n",
"\n",
"使用算法: 插入法\n",
"执行时间:14.7763秒, 路径长度:2366.95\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:1.0862秒, 路径长度:2671.07\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:12.8892秒, 路径长度:2269.30\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0354秒, 路径长度:2630.32\n",
"\n",
"测试实例: pa561.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0000秒, 路径长度:0.00\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0000秒, 路径长度:0.00\n",
"\n",
"使用算法: 插入法\n",
"评估过程出错: index 0 is out of bounds for axis 0 with size 0\n",
"\n",
"使用算法: EoH-TSP\n",
"评估过程出错: Sample larger than population or is negative\n",
"\n",
"使用算法: AAD-TSP\n",
"评估过程出错: index 0 is out of bounds for axis 0 with size 0\n",
"\n",
"使用算法: MEoH-TSP\n",
"评估过程出错: index 0 is out of bounds for axis 0 with size 0\n",
"\n",
"测试实例: u574.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0295秒, 路径长度:46881.87\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0288秒, 路径长度:46881.87\n",
"\n",
"使用算法: 插入法\n",
"执行时间:18.1802秒, 路径长度:44144.83\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:1.2306秒, 路径长度:46881.87\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:14.3463秒, 路径长度:39340.35\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0401秒, 路径长度:46620.28\n",
"\n",
"测试实例: rat575.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0309秒, 路径长度:8449.32\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0292秒, 路径长度:8449.32\n",
"\n",
"使用算法: 插入法\n",
"执行时间:18.5310秒, 路径长度:7853.86\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:1.2574秒, 路径长度:8430.71\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:10.7307秒, 路径长度:7398.48\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0477秒, 路径长度:7808.10\n",
"\n",
"测试实例: p654.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0402秒, 路径长度:43411.56\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0468秒, 路径长度:43411.56\n",
"\n",
"使用算法: 插入法\n",
"执行时间:28.7676秒, 路径长度:40418.68\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:1.5922秒, 路径长度:48824.31\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:21.2350秒, 路径长度:38249.53\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0448秒, 路径长度:41009.57\n",
"\n",
"测试实例: d657.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0403秒, 路径长度:62176.40\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0383秒, 路径长度:62176.40\n",
"\n",
"使用算法: 插入法\n",
"执行时间:29.4351秒, 路径长度:57906.66\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:1.7656秒, 路径长度:62176.40\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:17.0557秒, 路径长度:54801.59\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0487秒, 路径长度:63518.97\n",
"\n",
"测试实例: gr666.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0386秒, 路径长度:4110.90\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0407秒, 路径长度:4110.90\n",
"\n",
"使用算法: 插入法\n",
"执行时间:30.7468秒, 路径长度:3670.13\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:1.6598秒, 路径长度:4110.90\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:29.4371秒, 路径长度:3507.73\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0552秒, 路径长度:3920.88\n",
"\n",
"测试实例: u724.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0480秒, 路径长度:55223.20\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0465秒, 路径长度:55223.20\n",
"\n",
"使用算法: 插入法\n",
"执行时间:38.4876秒, 路径长度:50245.77\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:1.9141秒, 路径长度:52482.39\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:22.5706秒, 路径长度:47108.98\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0653秒, 路径长度:49538.41\n",
"\n",
"测试实例: rat783.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0604秒, 路径长度:11255.07\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0703秒, 路径长度:11255.07\n",
"\n",
"使用算法: 插入法\n",
"执行时间:52.2586秒, 路径长度:10301.88\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:2.3729秒, 路径长度:11255.07\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:40.4463秒, 路径长度:9468.73\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0693秒, 路径长度:11151.44\n",
"\n",
"测试实例: dsj1000.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0898秒, 路径长度:24630960.10\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0894秒, 路径长度:24630960.10\n",
"\n",
"使用算法: 插入法\n",
"执行时间:106.8209秒, 路径长度:22291166.04\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:3.4452秒, 路径长度:24630960.10\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:91.5279秒, 路径长度:21147745.60\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0580秒, 路径长度:23977443.37\n",
"\n",
"测试实例: pr1002.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0475秒, 路径长度:315596.59\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0601秒, 路径长度:315596.59\n",
"\n",
"使用算法: 插入法\n",
"执行时间:61.3033秒, 路径长度:302938.90\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:2.4444秒, 路径长度:325311.07\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:39.5357秒, 路径长度:278832.89\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0474秒, 路径长度:320713.45\n",
"\n",
"测试实例: u1060.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0540秒, 路径长度:281635.68\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0525秒, 路径长度:281635.68\n",
"\n",
"使用算法: 插入法\n",
"执行时间:69.3490秒, 路径长度:270377.38\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:1.9743秒, 路径长度:280689.98\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:46.9793秒, 路径长度:241924.19\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.1422秒, 路径长度:270485.28\n",
"\n",
"测试实例: vm1084.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0574秒, 路径长度:301469.23\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0561秒, 路径长度:301469.23\n",
"\n",
"使用算法: 插入法\n",
"执行时间:74.8310秒, 路径长度:277435.70\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:2.3008秒, 路径长度:302111.10\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:36.6202秒, 路径长度:262266.88\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0652秒, 路径长度:273437.36\n",
"\n",
"测试实例: pcb1173.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0644秒, 路径长度:70277.94\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0640秒, 路径长度:70277.94\n",
"\n",
"使用算法: 插入法\n",
"执行时间:90.3134秒, 路径长度:69010.68\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:2.6624秒, 路径长度:70279.94\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:47.8099秒, 路径长度:62709.99\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0695秒, 路径长度:71441.00\n",
"\n",
"测试实例: d1291.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0822秒, 路径长度:59941.24\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0835秒, 路径长度:59941.24\n",
"\n",
"使用算法: 插入法\n",
"执行时间:126.1144秒, 路径长度:59956.75\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:3.7137秒, 路径长度:59892.04\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:17.3619秒, 路径长度:55389.49\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0801秒, 路径长度:62211.60\n",
"\n",
"测试实例: rl1304.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.1415秒, 路径长度:339797.47\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.1519秒, 路径长度:339797.47\n",
"\n",
"使用算法: 插入法\n",
"执行时间:132.5021秒, 路径长度:314295.61\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:3.0462秒, 路径长度:335160.35\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:54.3530秒, 路径长度:284284.10\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.1450秒, 路径长度:303550.70\n",
"\n",
"测试实例: rl1323.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0873秒, 路径长度:332094.97\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.0857秒, 路径长度:332094.97\n",
"\n",
"使用算法: 插入法\n",
"执行时间:194.6513秒, 路径长度:341512.46\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:3.1830秒, 路径长度:331159.20\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:45.0123秒, 路径长度:299654.18\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0908秒, 路径长度:334237.62\n",
"\n",
"测试实例: nrw1379.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.0994秒, 路径长度:70015.46\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.1029秒, 路径长度:70015.46\n",
"\n",
"使用算法: 插入法\n",
"执行时间:161.1092秒, 路径长度:66216.21\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:3.4999秒, 路径长度:69794.61\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:100.5585秒, 路径长度:61838.87\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.1221秒, 路径长度:68362.91\n",
"\n",
"测试实例: fl1400.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.1201秒, 路径长度:26971.88\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.1193秒, 路径长度:26971.88\n",
"\n",
"使用算法: 插入法\n",
"执行时间:163.7658秒, 路径长度:22955.32\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:3.8870秒, 路径长度:27057.04\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:205.7018秒, 路径长度:22420.49\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.0907秒, 路径长度:23757.04\n",
"\n",
"测试实例: u1432.tsp\n",
"\n",
"使用算法: 贪心算法\n",
"执行时间:0.1072秒, 路径长度:188815.01\n",
"\n",
"使用算法: 最近邻算法\n",
"执行时间:0.1022秒, 路径长度:188815.01\n",
"\n",
"使用算法: 插入法\n",
"执行时间:162.7644秒, 路径长度:171110.62\n",
"\n",
"使用算法: EoH-TSP\n",
"执行时间:3.8517秒, 路径长度:196453.25\n",
"\n",
"使用算法: AAD-TSP\n",
"执行时间:85.9519秒, 路径长度:167338.88\n",
"\n",
"使用算法: MEoH-TSP\n",
"执行时间:0.1026秒, 路径长度:173489.17\n",
"\n",
"所有算法在各个实例上的表现:\n",
"\n",
"贪心算法:\n",
" burma14.tsp: 路径长度 = 38.69\n",
" ulysses16.tsp: 路径长度 = 104.73\n",
" ulysses22.tsp: 路径长度 = 89.64\n",
" dantzig42.tsp: 路径长度 = 0.00\n",
" att48.tsp: 路径长度 = 40526.42\n",
" eil51.tsp: 路径长度 = 513.61\n",
" berlin52.tsp: 路径长度 = 8980.92\n",
" st70.tsp: 路径长度 = 805.53\n",
" pr76.tsp: 路径长度 = 153461.92\n",
" eil76.tsp: 路径长度 = 711.99\n",
" gr96.tsp: 路径长度 = 707.09\n",
" rat99.tsp: 路径长度 = 1564.72\n",
" rd100.tsp: 路径长度 = 9941.16\n",
" kroB100.tsp: 路径长度 = 29155.04\n",
" kroD100.tsp: 路径长度 = 26950.46\n",
" kroA100.tsp: 路径长度 = 26856.39\n",
" kroC100.tsp: 路径长度 = 26327.36\n",
" kroE100.tsp: 路径长度 = 27587.19\n",
" eil101.tsp: 路径长度 = 825.24\n",
" lin105.tsp: 路径长度 = 20362.76\n",
" pr107.tsp: 路径长度 = 46678.15\n",
" pr124.tsp: 路径长度 = 69299.43\n",
" bier127.tsp: 路径长度 = 135751.78\n",
" ch130.tsp: 路径长度 = 7575.29\n",
" pr136.tsp: 路径长度 = 120777.86\n",
" gr137.tsp: 路径长度 = 1022.22\n",
" pr144.tsp: 路径长度 = 61650.72\n",
" kroA150.tsp: 路径长度 = 33609.87\n",
" ch150.tsp: 路径长度 = 8194.61\n",
" kroB150.tsp: 路径长度 = 32825.75\n",
" pr152.tsp: 路径长度 = 85702.95\n",
" u159.tsp: 路径长度 = 54669.03\n",
" rat195.tsp: 路径长度 = 2761.96\n",
" d198.tsp: 路径长度 = 18620.07\n",
" kroB200.tsp: 路径长度 = 36981.59\n",
" kroA200.tsp: 路径长度 = 35798.41\n",
" gr202.tsp: 路径长度 = 619.40\n",
" tsp225.tsp: 路径长度 = 4829.00\n",
" ts225.tsp: 路径长度 = 152493.55\n",
" pr226.tsp: 路径长度 = 94685.45\n",
" gr229.tsp: 路径长度 = 2014.71\n",
" gil262.tsp: 路径长度 = 3241.47\n",
" pr264.tsp: 路径长度 = 58022.86\n",
" a280.tsp: 路径长度 = 3148.11\n",
" pr299.tsp: 路径长度 = 59899.01\n",
" linhp318.tsp: 路径长度 = 54033.58\n",
" lin318.tsp: 路径长度 = 54033.58\n",
" rd400.tsp: 路径长度 = 19168.05\n",
" fl417.tsp: 路径长度 = 15114.12\n",
" gr431.tsp: 路径长度 = 2516.25\n",
" pr439.tsp: 路径长度 = 131282.09\n",
" pcb442.tsp: 路径长度 = 61984.05\n",
" d493.tsp: 路径长度 = 43646.38\n",
" att532.tsp: 路径长度 = 112099.45\n",
" ali535.tsp: 路径长度 = 2671.07\n",
" pa561.tsp: 路径长度 = 0.00\n",
" u574.tsp: 路径长度 = 46881.87\n",
" rat575.tsp: 路径长度 = 8449.32\n",
" p654.tsp: 路径长度 = 43411.56\n",
" d657.tsp: 路径长度 = 62176.40\n",
" gr666.tsp: 路径长度 = 4110.90\n",
" u724.tsp: 路径长度 = 55223.20\n",
" rat783.tsp: 路径长度 = 11255.07\n",
" dsj1000.tsp: 路径长度 = 24630960.10\n",
" pr1002.tsp: 路径长度 = 315596.59\n",
" u1060.tsp: 路径长度 = 281635.68\n",
" vm1084.tsp: 路径长度 = 301469.23\n",
" pcb1173.tsp: 路径长度 = 70277.94\n",
" d1291.tsp: 路径长度 = 59941.24\n",
" rl1304.tsp: 路径长度 = 339797.47\n",
" rl1323.tsp: 路径长度 = 332094.97\n",
" nrw1379.tsp: 路径长度 = 70015.46\n",
" fl1400.tsp: 路径长度 = 26971.88\n",
" u1432.tsp: 路径长度 = 188815.01\n",
" burma14.tsp: 执行时间 = 0.0000秒\n",
" ulysses16.tsp: 执行时间 = 0.0000秒\n",
" ulysses22.tsp: 执行时间 = 0.0000秒\n",
" dantzig42.tsp: 执行时间 = 0.0000秒\n",
" att48.tsp: 执行时间 = 0.0003秒\n",
" eil51.tsp: 执行时间 = 0.0005秒\n",
" berlin52.tsp: 执行时间 = 0.0004秒\n",
" st70.tsp: 执行时间 = 0.0006秒\n",
" pr76.tsp: 执行时间 = 0.0008秒\n",
" eil76.tsp: 执行时间 = 0.0009秒\n",
" gr96.tsp: 执行时间 = 0.0009秒\n",
" rat99.tsp: 执行时间 = 0.0013秒\n",
" rd100.tsp: 执行时间 = 0.0005秒\n",
" kroB100.tsp: 执行时间 = 0.0015秒\n",
" kroD100.tsp: 执行时间 = 0.0012秒\n",
" kroA100.tsp: 执行时间 = 0.0015秒\n",
" kroC100.tsp: 执行时间 = 0.0012秒\n",
" kroE100.tsp: 执行时间 = 0.0011秒\n",
" eil101.tsp: 执行时间 = 0.0011秒\n",
" lin105.tsp: 执行时间 = 0.0014秒\n",
" pr107.tsp: 执行时间 = 0.0016秒\n",
" pr124.tsp: 执行时间 = 0.0017秒\n",
" bier127.tsp: 执行时间 = 0.0015秒\n",
" ch130.tsp: 执行时间 = 0.0017秒\n",
" pr136.tsp: 执行时间 = 0.0020秒\n",
" gr137.tsp: 执行时间 = 0.0024秒\n",
" pr144.tsp: 执行时间 = 0.0025秒\n",
" kroA150.tsp: 执行时间 = 0.0025秒\n",
" ch150.tsp: 执行时间 = 0.0028秒\n",
" kroB150.tsp: 执行时间 = 0.0020秒\n",
" pr152.tsp: 执行时间 = 0.0025秒\n",
" u159.tsp: 执行时间 = 0.0022秒\n",
" rat195.tsp: 执行时间 = 0.0036秒\n",
" d198.tsp: 执行时间 = 0.0048秒\n",
" kroB200.tsp: 执行时间 = 0.0039秒\n",
" kroA200.tsp: 执行时间 = 0.0038秒\n",
" gr202.tsp: 执行时间 = 0.0033秒\n",
" tsp225.tsp: 执行时间 = 0.0040秒\n",
" ts225.tsp: 执行时间 = 0.0055秒\n",
" pr226.tsp: 执行时间 = 0.0047秒\n",
" gr229.tsp: 执行时间 = 0.0060秒\n",
" gil262.tsp: 执行时间 = 0.0067秒\n",
" pr264.tsp: 执行时间 = 0.0072秒\n",
" a280.tsp: 执行时间 = 0.0087秒\n",
" pr299.tsp: 执行时间 = 0.0083秒\n",
" linhp318.tsp: 执行时间 = 0.0101秒\n",
" lin318.tsp: 执行时间 = 0.0107秒\n",
" rd400.tsp: 执行时间 = 0.0146秒\n",
" fl417.tsp: 执行时间 = 0.0150秒\n",
" gr431.tsp: 执行时间 = 0.0173秒\n",
" pr439.tsp: 执行时间 = 0.0160秒\n",
" pcb442.tsp: 执行时间 = 0.0234秒\n",
" d493.tsp: 执行时间 = 0.0242秒\n",
" att532.tsp: 执行时间 = 0.0280秒\n",
" ali535.tsp: 执行时间 = 0.0268秒\n",
" pa561.tsp: 执行时间 = 0.0000秒\n",
" u574.tsp: 执行时间 = 0.0295秒\n",
" rat575.tsp: 执行时间 = 0.0309秒\n",
" p654.tsp: 执行时间 = 0.0402秒\n",
" d657.tsp: 执行时间 = 0.0403秒\n",
" gr666.tsp: 执行时间 = 0.0386秒\n",
" u724.tsp: 执行时间 = 0.0480秒\n",
" rat783.tsp: 执行时间 = 0.0604秒\n",
" dsj1000.tsp: 执行时间 = 0.0898秒\n",
" pr1002.tsp: 执行时间 = 0.0475秒\n",
" u1060.tsp: 执行时间 = 0.0540秒\n",
" vm1084.tsp: 执行时间 = 0.0574秒\n",
" pcb1173.tsp: 执行时间 = 0.0644秒\n",
" d1291.tsp: 执行时间 = 0.0822秒\n",
" rl1304.tsp: 执行时间 = 0.1415秒\n",
" rl1323.tsp: 执行时间 = 0.0873秒\n",
" nrw1379.tsp: 执行时间 = 0.0994秒\n",
" fl1400.tsp: 执行时间 = 0.1201秒\n",
" u1432.tsp: 执行时间 = 0.1072秒\n",
"\n",
"最近邻算法:\n",
" burma14.tsp: 路径长度 = 38.69\n",
" ulysses16.tsp: 路径长度 = 104.73\n",
" ulysses22.tsp: 路径长度 = 89.64\n",
" dantzig42.tsp: 路径长度 = 0.00\n",
" att48.tsp: 路径长度 = 40526.42\n",
" eil51.tsp: 路径长度 = 513.61\n",
" berlin52.tsp: 路径长度 = 8980.92\n",
" st70.tsp: 路径长度 = 805.53\n",
" pr76.tsp: 路径长度 = 153461.92\n",
" eil76.tsp: 路径长度 = 711.99\n",
" gr96.tsp: 路径长度 = 707.09\n",
" rat99.tsp: 路径长度 = 1564.72\n",
" rd100.tsp: 路径长度 = 9941.16\n",
" kroB100.tsp: 路径长度 = 29155.04\n",
" kroD100.tsp: 路径长度 = 26950.46\n",
" kroA100.tsp: 路径长度 = 26856.39\n",
" kroC100.tsp: 路径长度 = 26327.36\n",
" kroE100.tsp: 路径长度 = 27587.19\n",
" eil101.tsp: 路径长度 = 825.24\n",
" lin105.tsp: 路径长度 = 20362.76\n",
" pr107.tsp: 路径长度 = 46678.15\n",
" pr124.tsp: 路径长度 = 69299.43\n",
" bier127.tsp: 路径长度 = 135751.78\n",
" ch130.tsp: 路径长度 = 7575.29\n",
" pr136.tsp: 路径长度 = 120777.86\n",
" gr137.tsp: 路径长度 = 1022.22\n",
" pr144.tsp: 路径长度 = 61650.72\n",
" kroA150.tsp: 路径长度 = 33609.87\n",
" ch150.tsp: 路径长度 = 8194.61\n",
" kroB150.tsp: 路径长度 = 32825.75\n",
" pr152.tsp: 路径长度 = 85702.95\n",
" u159.tsp: 路径长度 = 54669.03\n",
" rat195.tsp: 路径长度 = 2761.96\n",
" d198.tsp: 路径长度 = 18620.07\n",
" kroB200.tsp: 路径长度 = 36981.59\n",
" kroA200.tsp: 路径长度 = 35798.41\n",
" gr202.tsp: 路径长度 = 619.40\n",
" tsp225.tsp: 路径长度 = 4829.00\n",
" ts225.tsp: 路径长度 = 152493.55\n",
" pr226.tsp: 路径长度 = 94685.45\n",
" gr229.tsp: 路径长度 = 2014.71\n",
" gil262.tsp: 路径长度 = 3241.47\n",
" pr264.tsp: 路径长度 = 58022.86\n",
" a280.tsp: 路径长度 = 3148.11\n",
" pr299.tsp: 路径长度 = 59899.01\n",
" linhp318.tsp: 路径长度 = 54033.58\n",
" lin318.tsp: 路径长度 = 54033.58\n",
" rd400.tsp: 路径长度 = 19168.05\n",
" fl417.tsp: 路径长度 = 15114.12\n",
" gr431.tsp: 路径长度 = 2516.25\n",
" pr439.tsp: 路径长度 = 131282.09\n",
" pcb442.tsp: 路径长度 = 61984.05\n",
" d493.tsp: 路径长度 = 43646.38\n",
" att532.tsp: 路径长度 = 112099.45\n",
" ali535.tsp: 路径长度 = 2671.07\n",
" pa561.tsp: 路径长度 = 0.00\n",
" u574.tsp: 路径长度 = 46881.87\n",
" rat575.tsp: 路径长度 = 8449.32\n",
" p654.tsp: 路径长度 = 43411.56\n",
" d657.tsp: 路径长度 = 62176.40\n",
" gr666.tsp: 路径长度 = 4110.90\n",
" u724.tsp: 路径长度 = 55223.20\n",
" rat783.tsp: 路径长度 = 11255.07\n",
" dsj1000.tsp: 路径长度 = 24630960.10\n",
" pr1002.tsp: 路径长度 = 315596.59\n",
" u1060.tsp: 路径长度 = 281635.68\n",
" vm1084.tsp: 路径长度 = 301469.23\n",
" pcb1173.tsp: 路径长度 = 70277.94\n",
" d1291.tsp: 路径长度 = 59941.24\n",
" rl1304.tsp: 路径长度 = 339797.47\n",
" rl1323.tsp: 路径长度 = 332094.97\n",
" nrw1379.tsp: 路径长度 = 70015.46\n",
" fl1400.tsp: 路径长度 = 26971.88\n",
" u1432.tsp: 路径长度 = 188815.01\n",
" burma14.tsp: 执行时间 = 0.0000秒\n",
" ulysses16.tsp: 执行时间 = 0.0000秒\n",
" ulysses22.tsp: 执行时间 = 0.0001秒\n",
" dantzig42.tsp: 执行时间 = 0.0000秒\n",
" att48.tsp: 执行时间 = 0.0002秒\n",
" eil51.tsp: 执行时间 = 0.0004秒\n",
" berlin52.tsp: 执行时间 = 0.0002秒\n",
" st70.tsp: 执行时间 = 0.0006秒\n",
" pr76.tsp: 执行时间 = 0.0007秒\n",
" eil76.tsp: 执行时间 = 0.0007秒\n",
" gr96.tsp: 执行时间 = 0.0011秒\n",
" rat99.tsp: 执行时间 = 0.0011秒\n",
" rd100.tsp: 执行时间 = 0.0012秒\n",
" kroB100.tsp: 执行时间 = 0.0013秒\n",
" kroD100.tsp: 执行时间 = 0.0007秒\n",
" kroA100.tsp: 执行时间 = 0.0010秒\n",
" kroC100.tsp: 执行时间 = 0.0015秒\n",
" kroE100.tsp: 执行时间 = 0.0008秒\n",
" eil101.tsp: 执行时间 = 0.0013秒\n",
" lin105.tsp: 执行时间 = 0.0012秒\n",
" pr107.tsp: 执行时间 = 0.0014秒\n",
" pr124.tsp: 执行时间 = 0.0015秒\n",
" bier127.tsp: 执行时间 = 0.0016秒\n",
" ch130.tsp: 执行时间 = 0.0020秒\n",
" pr136.tsp: 执行时间 = 0.0014秒\n",
" gr137.tsp: 执行时间 = 0.0017秒\n",
" pr144.tsp: 执行时间 = 0.0021秒\n",
" kroA150.tsp: 执行时间 = 0.0019秒\n",
" ch150.tsp: 执行时间 = 0.0020秒\n",
" kroB150.tsp: 执行时间 = 0.0024秒\n",
" pr152.tsp: 执行时间 = 0.0022秒\n",
" u159.tsp: 执行时间 = 0.0023秒\n",
" rat195.tsp: 执行时间 = 0.0037秒\n",
" d198.tsp: 执行时间 = 0.0040秒\n",
" kroB200.tsp: 执行时间 = 0.0034秒\n",
" kroA200.tsp: 执行时间 = 0.0039秒\n",
" gr202.tsp: 执行时间 = 0.0033秒\n",
" tsp225.tsp: 执行时间 = 0.0047秒\n",
" ts225.tsp: 执行时间 = 0.0044秒\n",
" pr226.tsp: 执行时间 = 0.0038秒\n",
" gr229.tsp: 执行时间 = 0.0057秒\n",
" gil262.tsp: 执行时间 = 0.0070秒\n",
" pr264.tsp: 执行时间 = 0.0068秒\n",
" a280.tsp: 执行时间 = 0.0075秒\n",
" pr299.tsp: 执行时间 = 0.0093秒\n",
" linhp318.tsp: 执行时间 = 0.0078秒\n",
" lin318.tsp: 执行时间 = 0.0097秒\n",
" rd400.tsp: 执行时间 = 0.0142秒\n",
" fl417.tsp: 执行时间 = 0.0148秒\n",
" gr431.tsp: 执行时间 = 0.0172秒\n",
" pr439.tsp: 执行时间 = 0.0185秒\n",
" pcb442.tsp: 执行时间 = 0.0198秒\n",
" d493.tsp: 执行时间 = 0.0226秒\n",
" att532.tsp: 执行时间 = 0.0257秒\n",
" ali535.tsp: 执行时间 = 0.0272秒\n",
" pa561.tsp: 执行时间 = 0.0000秒\n",
" u574.tsp: 执行时间 = 0.0288秒\n",
" rat575.tsp: 执行时间 = 0.0292秒\n",
" p654.tsp: 执行时间 = 0.0468秒\n",
" d657.tsp: 执行时间 = 0.0383秒\n",
" gr666.tsp: 执行时间 = 0.0407秒\n",
" u724.tsp: 执行时间 = 0.0465秒\n",
" rat783.tsp: 执行时间 = 0.0703秒\n",
" dsj1000.tsp: 执行时间 = 0.0894秒\n",
" pr1002.tsp: 执行时间 = 0.0601秒\n",
" u1060.tsp: 执行时间 = 0.0525秒\n",
" vm1084.tsp: 执行时间 = 0.0561秒\n",
" pcb1173.tsp: 执行时间 = 0.0640秒\n",
" d1291.tsp: 执行时间 = 0.0835秒\n",
" rl1304.tsp: 执行时间 = 0.1519秒\n",
" rl1323.tsp: 执行时间 = 0.0857秒\n",
" nrw1379.tsp: 执行时间 = 0.1029秒\n",
" fl1400.tsp: 执行时间 = 0.1193秒\n",
" u1432.tsp: 执行时间 = 0.1022秒\n",
"\n",
"插入法:\n",
" burma14.tsp: 路径长度 = 32.44\n",
" ulysses16.tsp: 路径长度 = 79.39\n",
" ulysses22.tsp: 路径长度 = 76.99\n",
" dantzig42.tsp: 路径长度 = inf\n",
" att48.tsp: 路径长度 = 37314.09\n",
" eil51.tsp: 路径长度 = 496.25\n",
" berlin52.tsp: 路径长度 = 9014.89\n",
" st70.tsp: 路径长度 = 778.99\n",
" pr76.tsp: 路径长度 = 125936.21\n",
" eil76.tsp: 路径长度 = 612.39\n",
" gr96.tsp: 路径长度 = 651.44\n",
" rat99.tsp: 路径长度 = 1482.02\n",
" rd100.tsp: 路径长度 = 8979.37\n",
" kroB100.tsp: 路径长度 = 25580.92\n",
" kroD100.tsp: 路径长度 = 25204.27\n",
" kroA100.tsp: 路径长度 = 24307.78\n",
" kroC100.tsp: 路径长度 = 25262.17\n",
" kroE100.tsp: 路径长度 = 25902.00\n",
" eil101.tsp: 路径长度 = 702.96\n",
" lin105.tsp: 路径长度 = 16934.62\n",
" pr107.tsp: 路径长度 = 52587.76\n",
" pr124.tsp: 路径长度 = 65318.19\n",
" bier127.tsp: 路径长度 = 140690.94\n",
" ch130.tsp: 路径长度 = 7279.21\n",
" pr136.tsp: 路径长度 = 109587.25\n",
" gr137.tsp: 路径长度 = 821.29\n",
" pr144.tsp: 路径长度 = 73033.13\n",
" kroA150.tsp: 路径长度 = 29966.54\n",
" ch150.tsp: 路径长度 = 7994.29\n",
" kroB150.tsp: 路径长度 = 31588.68\n",
" pr152.tsp: 路径长度 = 88530.82\n",
" u159.tsp: 路径长度 = 49981.41\n",
" rat195.tsp: 路径长度 = 2814.57\n",
" d198.tsp: 路径长度 = 17631.80\n",
" kroB200.tsp: 路径长度 = 35421.70\n",
" kroA200.tsp: 路径长度 = 35337.51\n",
" gr202.tsp: 路径长度 = 570.14\n",
" tsp225.tsp: 路径长度 = 4468.20\n",
" ts225.tsp: 路径长度 = 160009.16\n",
" pr226.tsp: 路径长度 = 91024.65\n",
" gr229.tsp: 路径长度 = 1825.83\n",
" gil262.tsp: 路径长度 = 2804.23\n",
" pr264.tsp: 路径长度 = 58225.34\n",
" a280.tsp: 路径长度 = 3101.79\n",
" pr299.tsp: 路径长度 = 58124.45\n",
" linhp318.tsp: 路径长度 = 49454.81\n",
" lin318.tsp: 路径长度 = 49454.81\n",
" rd400.tsp: 路径长度 = 18629.98\n",
" fl417.tsp: 路径长度 = 14179.84\n",
" gr431.tsp: 路径长度 = 2214.43\n",
" pr439.tsp: 路径长度 = 130067.88\n",
" pcb442.tsp: 路径长度 = 60891.83\n",
" d493.tsp: 路径长度 = 39982.31\n",
" att532.tsp: 路径长度 = 102201.61\n",
" ali535.tsp: 路径长度 = 2366.95\n",
" pa561.tsp: 路径长度 = inf\n",
" u574.tsp: 路径长度 = 44144.83\n",
" rat575.tsp: 路径长度 = 7853.86\n",
" p654.tsp: 路径长度 = 40418.68\n",
" d657.tsp: 路径长度 = 57906.66\n",
" gr666.tsp: 路径长度 = 3670.13\n",
" u724.tsp: 路径长度 = 50245.77\n",
" rat783.tsp: 路径长度 = 10301.88\n",
" dsj1000.tsp: 路径长度 = 22291166.04\n",
" pr1002.tsp: 路径长度 = 302938.90\n",
" u1060.tsp: 路径长度 = 270377.38\n",
" vm1084.tsp: 路径长度 = 277435.70\n",
" pcb1173.tsp: 路径长度 = 69010.68\n",
" d1291.tsp: 路径长度 = 59956.75\n",
" rl1304.tsp: 路径长度 = 314295.61\n",
" rl1323.tsp: 路径长度 = 341512.46\n",
" nrw1379.tsp: 路径长度 = 66216.21\n",
" fl1400.tsp: 路径长度 = 22955.32\n",
" u1432.tsp: 路径长度 = 171110.62\n",
" burma14.tsp: 执行时间 = 0.0004秒\n",
" ulysses16.tsp: 执行时间 = 0.0007秒\n",
" ulysses22.tsp: 执行时间 = 0.0010秒\n",
" dantzig42.tsp: 执行时间 = inf秒\n",
" att48.tsp: 执行时间 = 0.0124秒\n",
" eil51.tsp: 执行时间 = 0.0146秒\n",
" berlin52.tsp: 执行时间 = 0.0169秒\n",
" st70.tsp: 执行时间 = 0.0399秒\n",
" pr76.tsp: 执行时间 = 0.0511秒\n",
" eil76.tsp: 执行时间 = 0.0430秒\n",
" gr96.tsp: 执行时间 = 0.0923秒\n",
" rat99.tsp: 执行时间 = 0.1136秒\n",
" rd100.tsp: 执行时间 = 0.1072秒\n",
" kroB100.tsp: 执行时间 = 0.1206秒\n",
" kroD100.tsp: 执行时间 = 0.1119秒\n",
" kroA100.tsp: 执行时间 = 0.0981秒\n",
" kroC100.tsp: 执行时间 = 0.0983秒\n",
" kroE100.tsp: 执行时间 = 0.1026秒\n",
" eil101.tsp: 执行时间 = 0.1086秒\n",
" lin105.tsp: 执行时间 = 0.1155秒\n",
" pr107.tsp: 执行时间 = 0.1403秒\n",
" pr124.tsp: 执行时间 = 0.2034秒\n",
" bier127.tsp: 执行时间 = 0.2007秒\n",
" ch130.tsp: 执行时间 = 0.2338秒\n",
" pr136.tsp: 执行时间 = 0.2884秒\n",
" gr137.tsp: 执行时间 = 0.2412秒\n",
" pr144.tsp: 执行时间 = 0.3768秒\n",
" kroA150.tsp: 执行时间 = 0.3564秒\n",
" ch150.tsp: 执行时间 = 0.3516秒\n",
" kroB150.tsp: 执行时间 = 0.3577秒\n",
" pr152.tsp: 执行时间 = 0.3534秒\n",
" u159.tsp: 执行时间 = 0.3635秒\n",
" rat195.tsp: 执行时间 = 0.8294秒\n",
" d198.tsp: 执行时间 = 0.8395秒\n",
" kroB200.tsp: 执行时间 = 0.8084秒\n",
" kroA200.tsp: 执行时间 = 0.8769秒\n",
" gr202.tsp: 执行时间 = 0.8244秒\n",
" tsp225.tsp: 执行时间 = 1.2282秒\n",
" ts225.tsp: 执行时间 = 1.1208秒\n",
" pr226.tsp: 执行时间 = 1.1522秒\n",
" gr229.tsp: 执行时间 = 1.2088秒\n",
" gil262.tsp: 执行时间 = 1.8069秒\n",
" pr264.tsp: 执行时间 = 1.9408秒\n",
" a280.tsp: 执行时间 = 2.3356秒\n",
" pr299.tsp: 执行时间 = 2.6661秒\n",
" linhp318.tsp: 执行时间 = 3.1254秒\n",
" lin318.tsp: 执行时间 = 3.2427秒\n",
" rd400.tsp: 执行时间 = 6.5915秒\n",
" fl417.tsp: 执行时间 = 6.8940秒\n",
" gr431.tsp: 执行时间 = 7.8334秒\n",
" pr439.tsp: 执行时间 = 8.5353秒\n",
" pcb442.tsp: 执行时间 = 8.6915秒\n",
" d493.tsp: 执行时间 = 11.6873秒\n",
" att532.tsp: 执行时间 = 14.5934秒\n",
" ali535.tsp: 执行时间 = 14.7763秒\n",
" pa561.tsp: 执行时间 = inf秒\n",
" u574.tsp: 执行时间 = 18.1802秒\n",
" rat575.tsp: 执行时间 = 18.5310秒\n",
" p654.tsp: 执行时间 = 28.7676秒\n",
" d657.tsp: 执行时间 = 29.4351秒\n",
" gr666.tsp: 执行时间 = 30.7468秒\n",
" u724.tsp: 执行时间 = 38.4876秒\n",
" rat783.tsp: 执行时间 = 52.2586秒\n",
" dsj1000.tsp: 执行时间 = 106.8209秒\n",
" pr1002.tsp: 执行时间 = 61.3033秒\n",
" u1060.tsp: 执行时间 = 69.3490秒\n",
" vm1084.tsp: 执行时间 = 74.8310秒\n",
" pcb1173.tsp: 执行时间 = 90.3134秒\n",
" d1291.tsp: 执行时间 = 126.1144秒\n",
" rl1304.tsp: 执行时间 = 132.5021秒\n",
" rl1323.tsp: 执行时间 = 194.6513秒\n",
" nrw1379.tsp: 执行时间 = 161.1092秒\n",
" fl1400.tsp: 执行时间 = 163.7658秒\n",
" u1432.tsp: 执行时间 = 162.7644秒\n",
"\n",
"EoH-TSP:\n",
" burma14.tsp: 路径长度 = 38.69\n",
" ulysses16.tsp: 路径长度 = 76.87\n",
" ulysses22.tsp: 路径长度 = 89.64\n",
" dantzig42.tsp: 路径长度 = inf\n",
" att48.tsp: 路径长度 = 40526.42\n",
" eil51.tsp: 路径长度 = 513.61\n",
" berlin52.tsp: 路径长度 = 8980.92\n",
" st70.tsp: 路径长度 = 805.53\n",
" pr76.tsp: 路径长度 = 140565.41\n",
" eil76.tsp: 路径长度 = 669.24\n",
" gr96.tsp: 路径长度 = 707.09\n",
" rat99.tsp: 路径长度 = 1564.72\n",
" rd100.tsp: 路径长度 = 9941.16\n",
" kroB100.tsp: 路径长度 = 29155.04\n",
" kroD100.tsp: 路径长度 = 26950.46\n",
" kroA100.tsp: 路径长度 = 26856.39\n",
" kroC100.tsp: 路径长度 = 26327.36\n",
" kroE100.tsp: 路径长度 = 27587.19\n",
" eil101.tsp: 路径长度 = 847.59\n",
" lin105.tsp: 路径长度 = 20362.76\n",
" pr107.tsp: 路径长度 = 47029.63\n",
" pr124.tsp: 路径长度 = 69299.43\n",
" bier127.tsp: 路径长度 = 135751.78\n",
" ch130.tsp: 路径长度 = 7575.29\n",
" pr136.tsp: 路径长度 = 118776.81\n",
" gr137.tsp: 路径长度 = 1022.22\n",
" pr144.tsp: 路径长度 = 61650.72\n",
" kroA150.tsp: 路径长度 = 33609.87\n",
" ch150.tsp: 路径长度 = 8194.61\n",
" kroB150.tsp: 路径长度 = 32825.75\n",
" pr152.tsp: 路径长度 = 85702.95\n",
" u159.tsp: 路径长度 = 57436.69\n",
" rat195.tsp: 路径长度 = 2761.96\n",
" d198.tsp: 路径长度 = 18605.19\n",
" kroB200.tsp: 路径长度 = 36981.59\n",
" kroA200.tsp: 路径长度 = 35798.41\n",
" gr202.tsp: 路径长度 = 619.40\n",
" tsp225.tsp: 路径长度 = 4786.42\n",
" ts225.tsp: 路径长度 = 146183.10\n",
" pr226.tsp: 路径长度 = 94402.09\n",
" gr229.tsp: 路径长度 = 2014.71\n",
" gil262.tsp: 路径长度 = 3259.42\n",
" pr264.tsp: 路径长度 = 58328.28\n",
" a280.tsp: 路径长度 = 3182.09\n",
" pr299.tsp: 路径长度 = 60220.49\n",
" linhp318.tsp: 路径长度 = 54033.58\n",
" lin318.tsp: 路径长度 = 54033.58\n",
" rd400.tsp: 路径长度 = 19168.05\n",
" fl417.tsp: 路径长度 = 15256.42\n",
" gr431.tsp: 路径长度 = 2516.25\n",
" pr439.tsp: 路径长度 = 137778.50\n",
" pcb442.tsp: 路径长度 = 61234.77\n",
" d493.tsp: 路径长度 = 43710.70\n",
" att532.tsp: 路径长度 = 112099.45\n",
" ali535.tsp: 路径长度 = 2671.07\n",
" pa561.tsp: 路径长度 = inf\n",
" u574.tsp: 路径长度 = 46881.87\n",
" rat575.tsp: 路径长度 = 8430.71\n",
" p654.tsp: 路径长度 = 48824.31\n",
" d657.tsp: 路径长度 = 62176.40\n",
" gr666.tsp: 路径长度 = 4110.90\n",
" u724.tsp: 路径长度 = 52482.39\n",
" rat783.tsp: 路径长度 = 11255.07\n",
" dsj1000.tsp: 路径长度 = 24630960.10\n",
" pr1002.tsp: 路径长度 = 325311.07\n",
" u1060.tsp: 路径长度 = 280689.98\n",
" vm1084.tsp: 路径长度 = 302111.10\n",
" pcb1173.tsp: 路径长度 = 70279.94\n",
" d1291.tsp: 路径长度 = 59892.04\n",
" rl1304.tsp: 路径长度 = 335160.35\n",
" rl1323.tsp: 路径长度 = 331159.20\n",
" nrw1379.tsp: 路径长度 = 69794.61\n",
" fl1400.tsp: 路径长度 = 27057.04\n",
" u1432.tsp: 路径长度 = 196453.25\n",
" burma14.tsp: 执行时间 = 0.0120秒\n",
" ulysses16.tsp: 执行时间 = 0.0099秒\n",
" ulysses22.tsp: 执行时间 = 0.0160秒\n",
" dantzig42.tsp: 执行时间 = inf秒\n",
" att48.tsp: 执行时间 = 0.0307秒\n",
" eil51.tsp: 执行时间 = 0.0342秒\n",
" berlin52.tsp: 执行时间 = 0.0442秒\n",
" st70.tsp: 执行时间 = 0.0584秒\n",
" pr76.tsp: 执行时间 = 0.0688秒\n",
" eil76.tsp: 执行时间 = 0.0673秒\n",
" gr96.tsp: 执行时间 = 0.0769秒\n",
" rat99.tsp: 执行时间 = 0.1031秒\n",
" rd100.tsp: 执行时间 = 0.0986秒\n",
" kroB100.tsp: 执行时间 = 0.0981秒\n",
" kroD100.tsp: 执行时间 = 0.0862秒\n",
" kroA100.tsp: 执行时间 = 0.0825秒\n",
" kroC100.tsp: 执行时间 = 0.0842秒\n",
" kroE100.tsp: 执行时间 = 0.0972秒\n",
" eil101.tsp: 执行时间 = 0.0867秒\n",
" lin105.tsp: 执行时间 = 0.0962秒\n",
" pr107.tsp: 执行时间 = 0.1044秒\n",
" pr124.tsp: 执行时间 = 0.1309秒\n",
" bier127.tsp: 执行时间 = 0.1216秒\n",
" ch130.tsp: 执行时间 = 0.1343秒\n",
" pr136.tsp: 执行时间 = 0.1478秒\n",
" gr137.tsp: 执行时间 = 0.1438秒\n",
" pr144.tsp: 执行时间 = 0.1484秒\n",
" kroA150.tsp: 执行时间 = 0.1586秒\n",
" ch150.tsp: 执行时间 = 0.1501秒\n",
" kroB150.tsp: 执行时间 = 0.1559秒\n",
" pr152.tsp: 执行时间 = 0.1499秒\n",
" u159.tsp: 执行时间 = 0.1661秒\n",
" rat195.tsp: 执行时间 = 0.2163秒\n",
" d198.tsp: 执行时间 = 0.2329秒\n",
" kroB200.tsp: 执行时间 = 0.2069秒\n",
" kroA200.tsp: 执行时间 = 0.2339秒\n",
" gr202.tsp: 执行时间 = 0.2186秒\n",
" tsp225.tsp: 执行时间 = 0.2567秒\n",
" ts225.tsp: 执行时间 = 0.2431秒\n",
" pr226.tsp: 执行时间 = 0.3356秒\n",
" gr229.tsp: 执行时间 = 0.2919秒\n",
" gil262.tsp: 执行时间 = 0.3174秒\n",
" pr264.tsp: 执行时间 = 0.3232秒\n",
" a280.tsp: 执行时间 = 0.4066秒\n",
" pr299.tsp: 执行时间 = 0.3982秒\n",
" linhp318.tsp: 执行时间 = 0.4805秒\n",
" lin318.tsp: 执行时间 = 0.4752秒\n",
" rd400.tsp: 执行时间 = 0.6801秒\n",
" fl417.tsp: 执行时间 = 0.7143秒\n",
" gr431.tsp: 执行时间 = 0.7366秒\n",
" pr439.tsp: 执行时间 = 0.7811秒\n",
" pcb442.tsp: 执行时间 = 0.7918秒\n",
" d493.tsp: 执行时间 = 0.9321秒\n",
" att532.tsp: 执行时间 = 1.0641秒\n",
" ali535.tsp: 执行时间 = 1.0862秒\n",
" pa561.tsp: 执行时间 = inf秒\n",
" u574.tsp: 执行时间 = 1.2306秒\n",
" rat575.tsp: 执行时间 = 1.2574秒\n",
" p654.tsp: 执行时间 = 1.5922秒\n",
" d657.tsp: 执行时间 = 1.7656秒\n",
" gr666.tsp: 执行时间 = 1.6598秒\n",
" u724.tsp: 执行时间 = 1.9141秒\n",
" rat783.tsp: 执行时间 = 2.3729秒\n",
" dsj1000.tsp: 执行时间 = 3.4452秒\n",
" pr1002.tsp: 执行时间 = 2.4444秒\n",
" u1060.tsp: 执行时间 = 1.9743秒\n",
" vm1084.tsp: 执行时间 = 2.3008秒\n",
" pcb1173.tsp: 执行时间 = 2.6624秒\n",
" d1291.tsp: 执行时间 = 3.7137秒\n",
" rl1304.tsp: 执行时间 = 3.0462秒\n",
" rl1323.tsp: 执行时间 = 3.1830秒\n",
" nrw1379.tsp: 执行时间 = 3.4999秒\n",
" fl1400.tsp: 执行时间 = 3.8870秒\n",
" u1432.tsp: 执行时间 = 3.8517秒\n",
"\n",
"AAD-TSP:\n",
" burma14.tsp: 路径长度 = 31.23\n",
" ulysses16.tsp: 路径长度 = 73.99\n",
" ulysses22.tsp: 路径长度 = 76.89\n",
" dantzig42.tsp: 路径长度 = inf\n",
" att48.tsp: 路径长度 = 35738.05\n",
" eil51.tsp: 路径长度 = 465.91\n",
" berlin52.tsp: 路径长度 = 8178.40\n",
" st70.tsp: 路径长度 = 753.15\n",
" pr76.tsp: 路径长度 = 117831.22\n",
" eil76.tsp: 路径长度 = 622.71\n",
" gr96.tsp: 路径长度 = 623.53\n",
" rat99.tsp: 路径长度 = 1377.07\n",
" rd100.tsp: 路径长度 = 8864.57\n",
" kroB100.tsp: 路径长度 = 25392.46\n",
" kroD100.tsp: 路径长度 = 24565.25\n",
" kroA100.tsp: 路径长度 = 22957.15\n",
" kroC100.tsp: 路径长度 = 23253.25\n",
" kroE100.tsp: 路径长度 = 24382.95\n",
" eil101.tsp: 路径长度 = 702.70\n",
" lin105.tsp: 路径长度 = 18371.79\n",
" pr107.tsp: 路径长度 = 44905.53\n",
" pr124.tsp: 路径长度 = 63601.53\n",
" bier127.tsp: 路径长度 = 123202.55\n",
" ch130.tsp: 路径长度 = 7093.18\n",
" pr136.tsp: 路径长度 = 110347.07\n",
" gr137.tsp: 路径长度 = 849.98\n",
" pr144.tsp: 路径长度 = 61214.65\n",
" kroA150.tsp: 路径长度 = 28767.65\n",
" ch150.tsp: 路径长度 = 7161.32\n",
" kroB150.tsp: 路径长度 = 28181.08\n",
" pr152.tsp: 路径长度 = 80448.65\n",
" u159.tsp: 路径长度 = 50416.01\n",
" rat195.tsp: 路径长度 = 2490.60\n",
" d198.tsp: 路径长度 = 17340.94\n",
" kroB200.tsp: 路径长度 = 34372.39\n",
" kroA200.tsp: 路径长度 = 30701.99\n",
" gr202.tsp: 路径长度 = 529.30\n",
" tsp225.tsp: 路径长度 = 4169.09\n",
" ts225.tsp: 路径长度 = 139697.02\n",
" pr226.tsp: 路径长度 = 87543.04\n",
" gr229.tsp: 路径长度 = 1764.18\n",
" gil262.tsp: 路径长度 = 2757.03\n",
" pr264.tsp: 路径长度 = 56762.06\n",
" a280.tsp: 路径长度 = 2828.71\n",
" pr299.tsp: 路径长度 = 52408.65\n",
" linhp318.tsp: 路径长度 = 49153.11\n",
" lin318.tsp: 路径长度 = 49153.11\n",
" rd400.tsp: 路径长度 = 16651.71\n",
" fl417.tsp: 路径长度 = 13630.60\n",
" gr431.tsp: 路径长度 = 2153.00\n",
" pr439.tsp: 路径长度 = 118466.33\n",
" pcb442.tsp: 路径长度 = 54291.55\n",
" d493.tsp: 路径长度 = 38869.88\n",
" att532.tsp: 路径长度 = 99028.54\n",
" ali535.tsp: 路径长度 = 2269.30\n",
" pa561.tsp: 路径长度 = inf\n",
" u574.tsp: 路径长度 = 39340.35\n",
" rat575.tsp: 路径长度 = 7398.48\n",
" p654.tsp: 路径长度 = 38249.53\n",
" d657.tsp: 路径长度 = 54801.59\n",
" gr666.tsp: 路径长度 = 3507.73\n",
" u724.tsp: 路径长度 = 47108.98\n",
" rat783.tsp: 路径长度 = 9468.73\n",
" dsj1000.tsp: 路径长度 = 21147745.60\n",
" pr1002.tsp: 路径长度 = 278832.89\n",
" u1060.tsp: 路径长度 = 241924.19\n",
" vm1084.tsp: 路径长度 = 262266.88\n",
" pcb1173.tsp: 路径长度 = 62709.99\n",
" d1291.tsp: 路径长度 = 55389.49\n",
" rl1304.tsp: 路径长度 = 284284.10\n",
" rl1323.tsp: 路径长度 = 299654.18\n",
" nrw1379.tsp: 路径长度 = 61838.87\n",
" fl1400.tsp: 路径长度 = 22420.49\n",
" u1432.tsp: 路径长度 = 167338.88\n",
" burma14.tsp: 执行时间 = 0.0206秒\n",
" ulysses16.tsp: 执行时间 = 0.0241秒\n",
" ulysses22.tsp: 执行时间 = 0.0349秒\n",
" dantzig42.tsp: 执行时间 = inf秒\n",
" att48.tsp: 执行时间 = 0.0671秒\n",
" eil51.tsp: 执行时间 = 0.0815秒\n",
" berlin52.tsp: 执行时间 = 0.0888秒\n",
" st70.tsp: 执行时间 = 0.1202秒\n",
" pr76.tsp: 执行时间 = 0.1371秒\n",
" eil76.tsp: 执行时间 = 0.1221秒\n",
" gr96.tsp: 执行时间 = 0.2633秒\n",
" rat99.tsp: 执行时间 = 0.1965秒\n",
" rd100.tsp: 执行时间 = 0.2223秒\n",
" kroB100.tsp: 执行时间 = 0.2087秒\n",
" kroD100.tsp: 执行时间 = 0.1775秒\n",
" kroA100.tsp: 执行时间 = 0.1498秒\n",
" kroC100.tsp: 执行时间 = 0.1798秒\n",
" kroE100.tsp: 执行时间 = 0.1961秒\n",
" eil101.tsp: 执行时间 = 0.2088秒\n",
" lin105.tsp: 执行时间 = 0.1928秒\n",
" pr107.tsp: 执行时间 = 0.1547秒\n",
" pr124.tsp: 执行时间 = 0.1921秒\n",
" bier127.tsp: 执行时间 = 0.2196秒\n",
" ch130.tsp: 执行时间 = 0.2306秒\n",
" pr136.tsp: 执行时间 = 0.2450秒\n",
" gr137.tsp: 执行时间 = 0.5256秒\n",
" pr144.tsp: 执行时间 = 0.2336秒\n",
" kroA150.tsp: 执行时间 = 0.4204秒\n",
" ch150.tsp: 执行时间 = 0.3711秒\n",
" kroB150.tsp: 执行时间 = 0.4201秒\n",
" pr152.tsp: 执行时间 = 0.2910秒\n",
" u159.tsp: 执行时间 = 0.3241秒\n",
" rat195.tsp: 执行时间 = 0.5768秒\n",
" d198.tsp: 执行时间 = 0.6631秒\n",
" kroB200.tsp: 执行时间 = 0.7495秒\n",
" kroA200.tsp: 执行时间 = 0.8138秒\n",
" gr202.tsp: 执行时间 = 0.9231秒\n",
" tsp225.tsp: 执行时间 = 1.2099秒\n",
" ts225.tsp: 执行时间 = 0.6736秒\n",
" pr226.tsp: 执行时间 = 0.5864秒\n",
" gr229.tsp: 执行时间 = 1.0688秒\n",
" gil262.tsp: 执行时间 = 1.6125秒\n",
" pr264.tsp: 执行时间 = 0.7822秒\n",
" a280.tsp: 执行时间 = 1.4158秒\n",
" pr299.tsp: 执行时间 = 1.5654秒\n",
" linhp318.tsp: 执行时间 = 2.2263秒\n",
" lin318.tsp: 执行时间 = 2.0872秒\n",
" rd400.tsp: 执行时间 = 4.1070秒\n",
" fl417.tsp: 执行时间 = 7.6544秒\n",
" gr431.tsp: 执行时间 = 5.0698秒\n",
" pr439.tsp: 执行时间 = 4.3660秒\n",
" pcb442.tsp: 执行时间 = 4.3208秒\n",
" d493.tsp: 执行时间 = 7.6122秒\n",
" att532.tsp: 执行时间 = 9.7291秒\n",
" ali535.tsp: 执行时间 = 12.8892秒\n",
" pa561.tsp: 执行时间 = inf秒\n",
" u574.tsp: 执行时间 = 14.3463秒\n",
" rat575.tsp: 执行时间 = 10.7307秒\n",
" p654.tsp: 执行时间 = 21.2350秒\n",
" d657.tsp: 执行时间 = 17.0557秒\n",
" gr666.tsp: 执行时间 = 29.4371秒\n",
" u724.tsp: 执行时间 = 22.5706秒\n",
" rat783.tsp: 执行时间 = 40.4463秒\n",
" dsj1000.tsp: 执行时间 = 91.5279秒\n",
" pr1002.tsp: 执行时间 = 39.5357秒\n",
" u1060.tsp: 执行时间 = 46.9793秒\n",
" vm1084.tsp: 执行时间 = 36.6202秒\n",
" pcb1173.tsp: 执行时间 = 47.8099秒\n",
" d1291.tsp: 执行时间 = 17.3619秒\n",
" rl1304.tsp: 执行时间 = 54.3530秒\n",
" rl1323.tsp: 执行时间 = 45.0123秒\n",
" nrw1379.tsp: 执行时间 = 100.5585秒\n",
" fl1400.tsp: 执行时间 = 205.7018秒\n",
" u1432.tsp: 执行时间 = 85.9519秒\n",
"\n",
"MEoH-TSP:\n",
" burma14.tsp: 路径长度 = 38.80\n",
" ulysses16.tsp: 路径长度 = 104.65\n",
" ulysses22.tsp: 路径长度 = 91.92\n",
" dantzig42.tsp: 路径长度 = inf\n",
" att48.tsp: 路径长度 = 37686.87\n",
" eil51.tsp: 路径长度 = 458.95\n",
" berlin52.tsp: 路径长度 = 8835.06\n",
" st70.tsp: 路径长度 = 871.65\n",
" pr76.tsp: 路径长度 = 123787.14\n",
" eil76.tsp: 路径长度 = 577.27\n",
" gr96.tsp: 路径长度 = 573.48\n",
" rat99.tsp: 路径长度 = 1492.74\n",
" rd100.tsp: 路径长度 = 10510.18\n",
" kroB100.tsp: 路径长度 = 25679.71\n",
" kroD100.tsp: 路径长度 = 26072.81\n",
" kroA100.tsp: 路径长度 = 22683.29\n",
" kroC100.tsp: 路径长度 = 24294.06\n",
" kroE100.tsp: 路径长度 = 25221.45\n",
" eil101.tsp: 路径长度 = 720.41\n",
" lin105.tsp: 路径长度 = 19041.58\n",
" pr107.tsp: 路径长度 = 51115.61\n",
" pr124.tsp: 路径长度 = 68371.30\n",
" bier127.tsp: 路径长度 = 138054.23\n",
" ch130.tsp: 路径长度 = 7091.76\n",
" pr136.tsp: 路径长度 = 123247.21\n",
" gr137.tsp: 路径长度 = 901.99\n",
" pr144.tsp: 路径长度 = 60133.16\n",
" kroA150.tsp: 路径长度 = 31060.89\n",
" ch150.tsp: 路径长度 = 7589.72\n",
" kroB150.tsp: 路径长度 = 29848.14\n",
" pr152.tsp: 路径长度 = 92682.12\n",
" u159.tsp: 路径长度 = 51577.24\n",
" rat195.tsp: 路径长度 = 2575.61\n",
" d198.tsp: 路径长度 = 19454.99\n",
" kroB200.tsp: 路径长度 = 34079.08\n",
" kroA200.tsp: 路径长度 = 33224.38\n",
" gr202.tsp: 路径长度 = 559.57\n",
" tsp225.tsp: 路径长度 = 4430.19\n",
" ts225.tsp: 路径长度 = 131162.09\n",
" pr226.tsp: 路径长度 = 96212.20\n",
" gr229.tsp: 路径长度 = 1987.56\n",
" gil262.tsp: 路径长度 = 2748.90\n",
" pr264.tsp: 路径长度 = 59000.73\n",
" a280.tsp: 路径长度 = 3448.00\n",
" pr299.tsp: 路径长度 = 61338.05\n",
" linhp318.tsp: 路径长度 = 50085.92\n",
" lin318.tsp: 路径长度 = 50085.92\n",
" rd400.tsp: 路径长度 = 17599.52\n",
" fl417.tsp: 路径长度 = 13680.66\n",
" gr431.tsp: 路径长度 = 2263.78\n",
" pr439.tsp: 路径长度 = 134814.09\n",
" pcb442.tsp: 路径长度 = 58899.52\n",
" d493.tsp: 路径长度 = 43050.15\n",
" att532.tsp: 路径长度 = 103710.35\n",
" ali535.tsp: 路径长度 = 2630.32\n",
" pa561.tsp: 路径长度 = inf\n",
" u574.tsp: 路径长度 = 46620.28\n",
" rat575.tsp: 路径长度 = 7808.10\n",
" p654.tsp: 路径长度 = 41009.57\n",
" d657.tsp: 路径长度 = 63518.97\n",
" gr666.tsp: 路径长度 = 3920.88\n",
" u724.tsp: 路径长度 = 49538.41\n",
" rat783.tsp: 路径长度 = 11151.44\n",
" dsj1000.tsp: 路径长度 = 23977443.37\n",
" pr1002.tsp: 路径长度 = 320713.45\n",
" u1060.tsp: 路径长度 = 270485.28\n",
" vm1084.tsp: 路径长度 = 273437.36\n",
" pcb1173.tsp: 路径长度 = 71441.00\n",
" d1291.tsp: 路径长度 = 62211.60\n",
" rl1304.tsp: 路径长度 = 303550.70\n",
" rl1323.tsp: 路径长度 = 334237.62\n",
" nrw1379.tsp: 路径长度 = 68362.91\n",
" fl1400.tsp: 路径长度 = 23757.04\n",
" u1432.tsp: 路径长度 = 173489.17\n",
" burma14.tsp: 执行时间 = 0.0013秒\n",
" ulysses16.tsp: 执行时间 = 0.0009秒\n",
" ulysses22.tsp: 执行时间 = 0.0012秒\n",
" dantzig42.tsp: 执行时间 = inf秒\n",
" att48.tsp: 执行时间 = 0.0029秒\n",
" eil51.tsp: 执行时间 = 0.0030秒\n",
" berlin52.tsp: 执行时间 = 0.0040秒\n",
" st70.tsp: 执行时间 = 0.0047秒\n",
" pr76.tsp: 执行时间 = 0.0046秒\n",
" eil76.tsp: 执行时间 = 0.0046秒\n",
" gr96.tsp: 执行时间 = 0.0065秒\n",
" rat99.tsp: 执行时间 = 0.0057秒\n",
" rd100.tsp: 执行时间 = 0.0058秒\n",
" kroB100.tsp: 执行时间 = 0.0033秒\n",
" kroD100.tsp: 执行时间 = 0.0061秒\n",
" kroA100.tsp: 执行时间 = 0.0061秒\n",
" kroC100.tsp: 执行时间 = 0.0049秒\n",
" kroE100.tsp: 执行时间 = 0.0061秒\n",
" eil101.tsp: 执行时间 = 0.0051秒\n",
" lin105.tsp: 执行时间 = 0.0026秒\n",
" pr107.tsp: 执行时间 = 0.0060秒\n",
" pr124.tsp: 执行时间 = 0.0054秒\n",
" bier127.tsp: 执行时间 = 0.0069秒\n",
" ch130.tsp: 执行时间 = 0.0070秒\n",
" pr136.tsp: 执行时间 = 0.0074秒\n",
" gr137.tsp: 执行时间 = 0.0085秒\n",
" pr144.tsp: 执行时间 = 0.0070秒\n",
" kroA150.tsp: 执行时间 = 0.0089秒\n",
" ch150.tsp: 执行时间 = 0.0087秒\n",
" kroB150.tsp: 执行时间 = 0.0090秒\n",
" pr152.tsp: 执行时间 = 0.0070秒\n",
" u159.tsp: 执行时间 = 0.0079秒\n",
" rat195.tsp: 执行时间 = 0.0116秒\n",
" d198.tsp: 执行时间 = 0.0113秒\n",
" kroB200.tsp: 执行时间 = 0.0099秒\n",
" kroA200.tsp: 执行时间 = 0.0106秒\n",
" gr202.tsp: 执行时间 = 0.0095秒\n",
" tsp225.tsp: 执行时间 = 0.0128秒\n",
" ts225.tsp: 执行时间 = 0.0116秒\n",
" pr226.tsp: 执行时间 = 0.0140秒\n",
" gr229.tsp: 执行时间 = 0.0159秒\n",
" gil262.tsp: 执行时间 = 0.0139秒\n",
" pr264.tsp: 执行时间 = 0.0159秒\n",
" a280.tsp: 执行时间 = 0.0153秒\n",
" pr299.tsp: 执行时间 = 0.0157秒\n",
" linhp318.tsp: 执行时间 = 0.0260秒\n",
" lin318.tsp: 执行时间 = 0.0184秒\n",
" rd400.tsp: 执行时间 = 0.0254秒\n",
" fl417.tsp: 执行时间 = 0.0244秒\n",
" gr431.tsp: 执行时间 = 0.0273秒\n",
" pr439.tsp: 执行时间 = 0.0269秒\n",
" pcb442.tsp: 执行时间 = 0.0248秒\n",
" d493.tsp: 执行时间 = 0.0316秒\n",
" att532.tsp: 执行时间 = 0.0439秒\n",
" ali535.tsp: 执行时间 = 0.0354秒\n",
" pa561.tsp: 执行时间 = inf秒\n",
" u574.tsp: 执行时间 = 0.0401秒\n",
" rat575.tsp: 执行时间 = 0.0477秒\n",
" p654.tsp: 执行时间 = 0.0448秒\n",
" d657.tsp: 执行时间 = 0.0487秒\n",
" gr666.tsp: 执行时间 = 0.0552秒\n",
" u724.tsp: 执行时间 = 0.0653秒\n",
" rat783.tsp: 执行时间 = 0.0693秒\n",
" dsj1000.tsp: 执行时间 = 0.0580秒\n",
" pr1002.tsp: 执行时间 = 0.0474秒\n",
" u1060.tsp: 执行时间 = 0.1422秒\n",
" vm1084.tsp: 执行时间 = 0.0652秒\n",
" pcb1173.tsp: 执行时间 = 0.0695秒\n",
" d1291.tsp: 执行时间 = 0.0801秒\n",
" rl1304.tsp: 执行时间 = 0.1450秒\n",
" rl1323.tsp: 执行时间 = 0.0908秒\n",
" nrw1379.tsp: 执行时间 = 0.1221秒\n",
" fl1400.tsp: 执行时间 = 0.0907秒\n",
" u1432.tsp: 执行时间 = 0.1026秒\n",
"\n",
"各算法在不同实例上的路径长度:\n",
" 贪心算法 最近邻算法 插入法 EoH-TSP AAD-TSP MEoH-TSP\n",
"burma14 38.69 38.69 32.44 38.69 31.23 38.80\n",
"ulysses16 104.73 104.73 79.39 76.87 73.99 104.65\n",
"ulysses22 89.64 89.64 76.99 89.64 76.89 91.92\n",
"dantzig42 0.00 0.00 inf inf inf inf\n",
"att48 40526.42 40526.42 37314.09 40526.42 35738.05 37686.87\n",
"... ... ... ... ... ... ...\n",
"rl1304 339797.47 339797.47 314295.61 335160.35 284284.10 303550.70\n",
"rl1323 332094.97 332094.97 341512.46 331159.20 299654.18 334237.62\n",
"nrw1379 70015.46 70015.46 66216.21 69794.61 61838.87 68362.91\n",
"fl1400 26971.88 26971.88 22955.32 27057.04 22420.49 23757.04\n",
"u1432 188815.01 188815.01 171110.62 196453.25 167338.88 173489.17\n",
"\n",
"[74 rows x 6 columns]\n",
"\n",
"各算法在不同实例上的运行时长:\n",
" 贪心算法 最近邻算法 插入法 EoH-TSP AAD-TSP MEoH-TSP\n",
"burma14 0.0000 0.0000 0.0004 0.0120 0.0206 0.0013\n",
"ulysses16 0.0000 0.0000 0.0007 0.0099 0.0241 0.0009\n",
"ulysses22 0.0000 0.0001 0.0010 0.0160 0.0349 0.0012\n",
"dantzig42 0.0000 0.0000 inf inf inf inf\n",
"att48 0.0003 0.0002 0.0124 0.0307 0.0671 0.0029\n",
"... ... ... ... ... ... ...\n",
"rl1304 0.1415 0.1519 132.5021 3.0462 54.3530 0.1450\n",
"rl1323 0.0873 0.0857 194.6513 3.1830 45.0123 0.0908\n",
"nrw1379 0.0994 0.1029 161.1092 3.4999 100.5585 0.1221\n",
"fl1400 0.1201 0.1193 163.7658 3.8870 205.7018 0.0907\n",
"u1432 0.1072 0.1022 162.7644 3.8517 85.9519 0.1026\n",
"\n",
"[74 rows x 6 columns]\n"
]
}
],
"source": [
"# 用于存储每个算法在每个实例上的结果 \n",
"results = {}\n",
"results_time = {}\n",
"\n",
"# 遍历每个测试实例\n",
"for test_file in test_files:\n",
" print(f\"\\n测试实例: {test_file}\")\n",
" \n",
" # 加载数据\n",
" distances = load_tsp_data(test_file)\n",
" if distances is None:\n",
" continue\n",
" \n",
" # 这里可以添加多个待评估的TSP算法\n",
" algorithms = {\n",
" \"贪心算法\": greedy_tsp,\n",
" \"最近邻算法\": nearest_neighbor_tsp,\n",
" \"插入法\": insertion_tsp,\n",
" # \"tsp_01\": tsp_01,\n",
" \"EoH-TSP\": tsp_02,\n",
" \"AAD-TSP\": tsp_04,\n",
" \"MEoH-TSP\": tsp_06,\n",
" }\n",
" \n",
" # 评估每个算法\n",
" for alg_name, alg_func in algorithms.items():\n",
" print(f\"\\n使用算法: {alg_name}\")\n",
" score, etime = evaluate_tsp(alg_func, distances)\n",
" \n",
" if alg_name not in results:\n",
" results[alg_name] = {}\n",
" if alg_name not in results_time:\n",
" results_time[alg_name] = {}\n",
" results[alg_name][test_file] = score\n",
" results_time[alg_name][test_file] = etime\n",
"\n",
"# 打印总结果\n",
"print(\"\\n所有算法在各个实例上的表现:\")\n",
"for alg_name, scores in results.items():\n",
" print(f\"\\n{alg_name}:\")\n",
" for test_file, score in scores.items():\n",
" print(f\" {test_file}: 路径长度 = {score:.2f}\")\n",
" for test_file, etime in results_time[alg_name].items():\n",
" print(f\" {test_file}: 执行时间 = {etime:.4f}秒\")\n",
"\n",
"# 将结果整理成表格\n",
"data = {}\n",
"for test_file in test_files:\n",
" instance_name = test_file.replace('.tsp', '')\n",
" data[instance_name] = {}\n",
" for alg_name in results:\n",
" if test_file in results[alg_name]:\n",
" data[instance_name][alg_name] = results[alg_name][test_file]\n",
"\n",
"# 将运行时间整理成表格\n",
"data_time = {}\n",
"for test_file in test_files:\n",
" instance_name = test_file.replace('.tsp', '')\n",
" data_time[instance_name] = {}\n",
" for alg_name in results_time:\n",
" if test_file in results_time[alg_name]:\n",
" data_time[instance_name][alg_name] = results_time[alg_name][test_file]\n",
"\n",
"import random\n",
"\n",
"# 转换为DataFrame\n",
"df_results = pd.DataFrame(data).T\n",
"df_time = pd.DataFrame(data_time).T\n",
"\n",
"# 打印结果表格\n",
"print(\"\\n各算法在不同实例上的路径长度:\")\n",
"print(df_results.round(2)) # 路径长度保留2位小数\n",
"# 统计每个算法的运行时长\n",
"print(\"\\n各算法在不同实例上的运行时长:\")\n",
"print(df_time.round(4)) # 运行时间保留4位小数\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"各算法在不同实例上的路径长度:\n",
" 贪心算法 最近邻算法 插入法 EoH-TSP AAD-TSP MEoH-TSP\n",
"burma14 38.69 38.69 32.44 38.69 31.23 38.80\n",
"ulysses16 104.73 104.73 79.39 76.87 73.99 104.65\n",
"ulysses22 89.64 89.64 76.99 89.64 76.89 91.92\n",
"att48 40526.42 40526.42 37314.09 40526.42 35738.05 37686.87\n",
"eil51 513.61 513.61 496.25 513.61 465.91 458.95\n",
"... ... ... ... ... ... ...\n",
"rl1304 339797.47 339797.47 314295.61 335160.35 284284.10 303550.70\n",
"rl1323 332094.97 332094.97 341512.46 331159.20 299654.18 334237.62\n",
"nrw1379 70015.46 70015.46 66216.21 69794.61 61838.87 68362.91\n",
"fl1400 26971.88 26971.88 22955.32 27057.04 22420.49 23757.04\n",
"u1432 188815.01 188815.01 171110.62 196453.25 167338.88 173489.17\n",
"\n",
"[72 rows x 6 columns]\n",
"\n",
"各算法在不同实例上的运行时长:\n",
" 贪心算法 最近邻算法 插入法 EoH-TSP AAD-TSP MEoH-TSP\n",
"burma14 0.0000 0.0000 0.0004 0.0120 0.0206 0.0013\n",
"ulysses16 0.0000 0.0000 0.0007 0.0099 0.0241 0.0009\n",
"ulysses22 0.0000 0.0001 0.0010 0.0160 0.0349 0.0012\n",
"att48 0.0003 0.0002 0.0124 0.0307 0.0671 0.0029\n",
"eil51 0.0005 0.0004 0.0146 0.0342 0.0815 0.0030\n",
"... ... ... ... ... ... ...\n",
"rl1304 0.1415 0.1519 132.5021 3.0462 54.3530 0.1450\n",
"rl1323 0.0873 0.0857 194.6513 3.1830 45.0123 0.0908\n",
"nrw1379 0.0994 0.1029 161.1092 3.4999 100.5585 0.1221\n",
"fl1400 0.1201 0.1193 163.7658 3.8870 205.7018 0.0907\n",
"u1432 0.1072 0.1022 162.7644 3.8517 85.9519 0.1026\n",
"\n",
"[72 rows x 6 columns]\n"
]
}
],
"source": [
"# 创建排序用的临时索引\n",
"temp_results_idx = pd.Index([int(''.join(filter(str.isdigit, idx))) for idx in df_results.index])\n",
"temp_time_idx = pd.Index([int(''.join(filter(str.isdigit, idx))) for idx in df_time.index])\n",
"\n",
"# 按数字排序但保留原始名称\n",
"df_results = df_results.iloc[temp_results_idx.argsort()]\n",
"df_time = df_time.iloc[temp_time_idx.argsort()]\n",
"\n",
"# 删掉包含inf的行\n",
"df_results = df_results[~df_results.isin([float('inf')]).any(axis=1)]\n",
"df_time = df_time[~df_time.isin([float('inf')]).any(axis=1)]\n",
"\n",
"# 打印结果表格\n",
"print(\"\\n各算法在不同实例上的路径长度:\")\n",
"print(df_results.round(2)) # 路径长度保留2位小数\n",
"# 统计每个算法的运行时长 \n",
"print(\"\\n各算法在不同实例上的运行时长:\")\n",
"print(df_time.round(4)) # 运行时间保留4位小数"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"各算法在不同实例上的排名:\n",
" 贪心算法 最近邻算法 插入法 EoH-TSP AAD-TSP MEoH-TSP\n",
"burma14 3 3 2 3 1 6\n",
"ulysses16 5 5 3 2 1 4\n",
"ulysses22 3 3 2 3 1 6\n",
"att48 4 4 2 4 1 3\n",
"eil51 4 4 3 4 2 1\n",
"... ... ... ... ... ... ...\n",
"rl1304 5 5 3 4 1 2\n",
"rl1323 3 3 6 2 1 5\n",
"nrw1379 5 5 2 4 1 3\n",
"fl1400 4 4 2 6 1 3\n",
"u1432 4 4 2 6 1 3\n",
"\n",
"[72 rows x 6 columns]\n",
"\n",
"各算法在所有实例上的平均排名:\n",
"贪心算法 3.82\n",
"最近邻算法 3.82\n",
"插入法 2.75\n",
"EoH-TSP 4.06\n",
"AAD-TSP 1.19\n",
"MEoH-TSP 3.25\n",
"dtype: float64\n",
"\n",
"各算法在不同实例上的运行时长排名:\n",
" 贪心算法 最近邻算法 插入法 EoH-TSP AAD-TSP MEoH-TSP\n",
"burma14 2 1 3 5 6 4\n",
"ulysses16 2 1 3 5 6 4\n",
"ulysses22 1 2 3 5 6 4\n",
"att48 2 1 4 5 6 3\n",
"eil51 2 1 4 5 6 3\n",
"... ... ... ... ... ... ...\n",
"rl1304 1 3 6 4 5 2\n",
"rl1323 2 1 6 4 5 3\n",
"nrw1379 1 2 6 4 5 3\n",
"fl1400 3 2 5 4 6 1\n",
"u1432 3 1 6 4 5 2\n",
"\n",
"[72 rows x 6 columns]\n",
"\n",
"各算法在所有实例上的平均运行时长排名:\n",
"贪心算法 1.74\n",
"最近邻算法 1.43\n",
"插入法 5.44\n",
"EoH-TSP 4.12\n",
"AAD-TSP 5.39\n",
"MEoH-TSP 2.88\n",
"dtype: float64\n",
"\n",
"各算法在所有实例上的平均路径长度:\n",
"贪心算法 400722.45\n",
"最近邻算法 400722.45\n",
"插入法 365681.25\n",
"EoH-TSP 400751.63\n",
"AAD-TSP 345580.06\n",
"MEoH-TSP 389168.81\n",
"dtype: float64\n",
"\n",
"各算法在所有实例上的平均运行时长:\n",
"贪心算法 0.0213\n",
"最近邻算法 0.0215\n",
"插入法 23.1702\n",
"EoH-TSP 0.8359\n",
"AAD-TSP 13.9758\n",
"MEoH-TSP 0.0279\n",
"dtype: float64\n"
]
}
],
"source": [
"# 统计df_results中每行的分别排行，得到每个算法的排名\n",
"rank_results = df_results.rank(axis=1, method='min', ascending=True)\n",
"rank_results = rank_results.astype(int) # 转换为整数类型\n",
"print(\"\\n各算法在不同实例上的排名:\")\n",
"print(rank_results)\n",
"# 计算得到各算法在所有实例上的平均排名\n",
"mean_rank = rank_results.mean(axis=0)\n",
"print(\"\\n各算法在所有实例上的平均排名:\")\n",
"print(mean_rank.round(2)) # 平均排名保留2位小数\n",
"\n",
"# 统计df_time中每行的分别排行，得到每个算法的排名\n",
"rank_time = df_time.rank(axis=1, method='min', ascending=True)\n",
"rank_time = rank_time.astype(int) # 转换为整数类型\n",
"print(\"\\n各算法在不同实例上的运行时长排名:\")\n",
"print(rank_time)\n",
"# 计算得到各算法在所有实例上的平均排名\n",
"mean_rank_time = rank_time.mean(axis=0)\n",
"print(\"\\n各算法在所有实例上的平均运行时长排名:\")\n",
"print(mean_rank_time.round(2)) # 平均排名保留2位小数\n",
"# 计算每个算法的平均路径长度\n",
"mean_results = df_results.mean(axis=0)\n",
"print(\"\\n各算法在所有实例上的平均路径长度:\")\n",
"print(mean_results.round(2)) # 平均路径长度保留2位小数\n",
"# 计算每个算法的平均运行时长\n",
"mean_time = df_time.mean(axis=0)\n",
"print(\"\\n各算法在所有实例上的平均运行时长:\")\n",
"print(mean_time.round(4)) # 平均运行时长保留4位小数"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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},
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QfClfr149JCYm4qKW1Zj++ecfnfVGjx4NAFi3bp08rKi4DhcCmBAQERERFR2tWgFKM43oViqBLOP081P16tXxySef4OnTp+jatSvu3r2rtVxMTIxR7U6YMAEAMHz4cDx//jzH+cePH+PmzZsml9dMHJ4+fXq2PQquXr2KTZs26YyrYcOGaNSoETZv3owdO3agUaNGaNCggVH3VpRwDgERERFRUTFsGPC/lXLyLD09s70CsmDBAqSlpWH58uWoXr062rRpg7p168LOzg5Pnz7F5cuXcf78eTg5OaFu3boGtdmlSxfMnDkTc+bMQZUqVdClSxdUrFgRz58/R2hoKP755x/MnTsXNWvWNKm8r68vfvnlFxw4cAANGjRA165dER0djV9//RWdOnXC3r17dcY2evRoeQ5Bce4dAABJ6JsWTnrFxsbC2dkZKpUKTk5OhR0OERERFZLk5GSEhYXBx8cHNjY2eWusTRvg5Mm8LT+qVAJvvgkcO5a3WExw4cIFrF69GsePH8ejR4+QmpoKV1dXvP766+jWrRs++OADefOy8PBw+Pj4wNfXFxs3btTZ5qFDh7BixQqcPn0aMTExcHNzg4+PD7p16wZfX98cE4ONKZ+YmIiAgAD8/PPPiIqKQuXKlTFx4kRUq1YN7dq1g5+fH/z9/XPEFB8fD1dXV1haWuK///4rsGdBQz9rxjynMiHIAyYEREREBJg5IQgNBerUAVJSTG/D2hq4dg2oUiVvsZBOZ8+eRdOmTTFs2DBs2LChwK6bHwkB5xAQERERFSVVqgCBgYCOlXdyJUmZ9ZkM5Kuvv/4aADBmzJhCjiTvOIeAiIiIqKgZOBAQAhg+HMjIMGz4kFIJWFhkJgMDB+Z/jCXQgwcP8Msvv+D69evYtm0bunTpgiZNmhR2WHnGHgIiIiKiomjQoMxhP/9b41/n6kOa4y1aZJZnMpBv7t27h2nTpmHXrl145513EBgYWNghmQV7CIiIiIiKqipVMicGX7yY+c3/iRPA9euZOxBbWmbuM9CyZeZqQg0bFna0r7y2bdviVZx+y4SAiIiIqKhr2DD7A79aXSA7EFPJwE8SERERUXHDZIDMiJ8mIiIiIqISjAkBEREREVEJxoSAiIiIiKgEY0JARERERFSCMSEgIiIiIirBmBAQEREREZVgTAiIiIiIiEowJgRERERERVBISOYGxca+QkIKJ97w8HBIkgRJktCjRw+tZY4ePQpJkjBmzBj52NChQ+V6586d01qvbdu2kCQJjx8/zpfYSzruVExERERUxISEANWqmV7/zh2galXzxWOsffv24fjx42jdurVR9aZMmYIjR47kU1SkC3sIiIiIiIqYuLjCrZ8X3t7eUCgUmDJlilH1KleujKCgIBw4cCCfIiNdmBAQERERkdlUr14dH3zwAU6fPo3ff//d4Hp+fn5QKpWYOnUqhBD5GCG9jAkBEREREZnV7NmzYW1tjS+//BIZGRkG1alatSpGjRqF4OBg/Pzzz/kcIWXFhICIiIiIzKpChQoYN24cbt++jfXr1xtcb9asWbC3t8fMmTORmpqajxFSVkwIiIiIiMjspk+fDmdnZwQEBCAxMdGgOl5eXpg8eTLCw8Px3Xff5XOEpMGEgIiIiIjMztXVFVOmTEFkZCSWLVtmcL0vvvgCHh4emDdvHmJjY/MvQJIxISAiIiKifDFp0iSULVsWixYtwvPnzw2q4+joiOnTpyMqKgqLFi3K5wgJYEJARERERPnE1tYW/v7+UKlU+OqrrwyuN3bsWPj4+OCbb77hZmQFgAkBEREREeWb4cOHo0aNGli1ahUePHhgUB0rKyvMmTMHiYmJCAgIyOcIqdgmBIsWLZK3uT59+rTB9Z4+fYr58+ejb9++8PHxkdsgIiIiIvOzsLDAV199hZSUFMyePdvgeoMGDUL9+vWxbt06hIeH51+AVDwTgps3b8rLUhnrxo0b+PLLL/H777/DysoKdnZ2+RAhEREREWn07t0bzZs3x927dw2uI0kSFixYgPT0dNy/fz8fo6NilxBkZGTA19cX9erVQ+/evY2uX7NmTRw7dgwqlQq3b99G+fLl8yFKKgwzZsyAJEmcgESkx5gxYyBJEjZs2FDYoRBRCbNw4UKj63Tu3BlvvfVWPkRDWRW7hGDhwoUIDg7Ghg0bYGFhYXR9T09PtG7dGo6OjvkQHRWWiIgILF26FB4eHhg3bpzOcnfv3sXo0aPh4+MDGxsblC5dGp07d8aOHTvyHENSUhK++eYbtGrVCm5ubrC0tISzszMaNWqE6dOnGz0p6sWLFyhbtqw8rM3f3z/PMRrj3r17sLe3l6+/cePGPLUnhMD69evRqlUruLq6ws7ODtWqVcMnn3yCJ0+e6KyXnp6Ow4cPY/HixRgwYACqVasGhUIBSZIwdOjQPMVU2H7//Xd07twZpUuXho2NDXx8fDB69GiEhobqrBMeHi7/P9H3mjFjhtb6X375JaysrDBz5kwkJCTk160RUQnk7e0NIQQOHDig9XyrVq0ghIAQAqtXr5aPb9y4EUIINGvWTGu9w4cPy/W8vLzyJfYSTxQjV69eFVZWVmL27NlCCCF8fX0FAHHq1CmT26xevbow9W1QqVQCgFCpVCZfn8xjyJAhAoBYvHixzjL79u0TdnZ2AoAAIJycnIRCoZB/HjZsmFCr1SZdPzw8XFStWlVuC4BwdnbO1r6zs7M4duyYwW1qPt+al5+fn0mxmUKtVot27dplu35gYKDJ7SUnJ4vOnTvLbSmVSuHg4CD/7ObmJs6fP6+1blhYWLY4sr58fX1NjqkwqdVqMWzYMPk+FAqFcHJykn+2s7MT+/bt01o36/vh7u4uPD09tb4WLFig8/qjR48WAERAQEB+3SJRiZOUlCRu3LghkpKSzNLenTtCAKa/7twxSxhUBBn6WTPmObXY9BCkp6dj6NChqFmzJqZOnVooMaSkpCA2Njbbiwrfo0eP8PPPP8PKygrDhw/XWiYsLAzvvfceEhMT0aJFC9y+fRsqlQoqlQqzZs0CAAQGBmLx4sUmxTBkyBCEhITAysoKK1euRFxcHGJiYpCUlITdu3ejXLlyUKlUeO+995CUlJRre3/99Rd+/PFHNG/e3KR48mrNmjUICgrCm2++aZb2Jk+ejL/++guWlpZYuXIlEhISEBcXh3PnzqFGjRp4/vw5evToofN3ytHRES1btsTEiRPx448/on79+maJK69MXZRg8eLFCAwMBAD4+fnJn8Vbt27hzTffRGJiIt577z2EhYXpbefcuXN4/Pix1teUKVN01hszZgwAYMWKFUhJSTE6fiLKf1WrAnfuABcuGP+6cyezPpHBzJWt5LeAgAChVCrFhQsX5GMF3UPg5+en9VtK9hAULs3/l169euksM3jwYAFAeHl5iRcvXuQ4/+GHH8q9BtHR0UZdPzw8PNdv8Q8dOiSXOXDggN72YmNjRYUKFYSVlZW4du1agfcQPHjwQDg5OQkXFxdx+vTpPPcQ3L59W1hYWAgAYv78+TnO3717V9ja2goAYsaMGTnOZ2Rk5Oi5adOmTZHoIdC8N8aIjo4Wjo6OAoAYPXq01vNeXl4CgBg8eHCO81l7CMLCwkwNXdSpU0cAEJs3bza5DSL6f+buISDSpcT2EAQHB2Pu3Ln47LPP0LBhw0KLY9q0afI3eSqVCg8fPiy0WCiT+N+4dCBzeTJtEhIS5DkCY8eORalSpXKUmTZtGgAgNjYWu3btMiqG//77T/7zG2+8obVMkyZN5D/Hx8frbW/KlCl48OABpk6ditq1axsVizmMHj0asbGxWLJkCTw9PfPc3ubNm5GRkQEHBwd8/PHHOc5XqlQJ/fv3BwBs2rQpx3nNfIG8evz4MaZOnYp69erB2dkZNjY2qFSpEkaOHIkbN27kuX1D7dy5E3FxcQD+/3OXlYuLi/wN/o4dO/JtnP/AgQMBZPYGERFRyVYsEgJfX19Urly5wCdVvsza2hpOTk7ZXlS4rl27hoiICACZk5W0OXHihDxMp2vXrlrLeHt7o2bNmgCAgwcPGhVDpUqV5D+fP39ea5mzZ88CyHy4bdCggc62jh07htWrV6NGjRr48ssvjYrDHH766Sf8+eefaNeuHYYNG2aWNv/++28AQOvWrXUuFaz5/3L//n3cvn3bLNfNau/evahatSoWLlyIK1euICkpCUqlEmFhYVi/fj0aNGiAn376yezX1UbzftSqVQsVK1bUWkbzfiQlJeHEiRP5Ekfr1q0BAP/++6+coBARUclULBKC4OBg3Lp1CzY2NtlW0fjxxx8BAM2bN4ckSUZ/s0vF3/HjxwEA5cuX17nywLVr1+Q/6/vGvU6dOgCA69evGxVD6dKl8e677wIA5s+fj1WrVsm9AGlpadizZw98fX0BAJ9++mm2BCKrpKQkjBw5EkDmt7bW1tZGxZFXT548weTJk2FjY2PWb40176fm/dUm6zlj3//cnD17Fn369EF8fDxGjx6NmzdvIikpCfHx8bh//z4++ugjpKamYsSIEToTOnPSfB7N8X70798frq6usLKyQpkyZdC9e3ds2rQJ6enpucbRqFEjKJVKZGRk4N9//zXiDoiI6FWjLOwADDFixAitx48fP46QkBC888478PDwgLe3d8EGRoXuzJkzAIB69erpLBMZGQkgcyiGvo3oypUrl628MdatW4eEhAT89ddfGD9+PMaPHw9nZ2fExcVBrVbj9ddfh7+/v/zAr82MGTMQGhqK0aNH6+ztyE/jxo1DdHQ05s+fjypVqpilzbi4OPnbZ837q03Wc6a8//qMHz8eqampmDlzZo4dMitUqIBVq1ZBqVRixYoVmDt3br5/saC5P33vh52dHUqVKoWYmBi978fZs2fh6OgIS0tLPH78GPv378f+/fuxcuVK7Nq1C2XKlNFZ19bWFtWrV8f169dx6tQpdOnSxfSbIiKiYq1YJATr1q3Tenzo0KEICQnBtGnTcqxdGxUVhaioKLi7u8Pd3b0gwqRCoHlY8vDw0FlG80Ca267UmvOmDJ9wcXHB77//jlmzZmHp0qUQQkClUsnn4+PjERUVhYyMDK37Z5w5cwbLli1DmTJlTNq4Ja+2bduGHTt2oG7duvjss8/M1m7W91Lf+5/1nDmHrwQHB+PcuXOwtLTEp59+qrPckCFDsGLFChw6dEjn/yNzMebzGBMTk+P9sLGxwdixY/Hee++hYcOG8tDFkJAQLF++HN999x3Onj2L7t274+zZs1Aqdf81r/m70dxJGBERFS/FIiEwxcqVKxEQEAA/P78ccw+ybmakmRCa9djXX3/NJKKYePbsGQDA1dW1UOO4dOkSevbsiUePHuGjjz7CmDFjUKlSJTx+/Bh//PEH/Pz8MG3aNBw/fhx79+6FQvH/o/VSU1MxfPhwqNVqfPvtt3B2di7Q2KOjozF+/HgoFAqsXbtW7wNkfjHHpGFtNOPv1Wo1qlevrrNcRkYGgMwJ6M+fP0fp0qXlc40bN851AQFdw9XefPNN/P7778aGneu1vvvuuxzHq1atipUrV6JSpUr49NNPcenSJfz00086l+IF/v/3RvN7REREJdMrmxDoo5l7oOuYv78/E4JiIjk5GQD0jrfX7EqdmJioty3NeWN3sY6Li0PXrl3x5MkTzJo1CwEBAfK5SpUqYeLEiahTpw46duyIP//8E4GBgdmGwc2ePRs3btxAz5490adPH6OubQ4TJkzA06dPMXHixGyrIZlD1vdS3/ufdSUdc+4irvnmOyMjQ+9uyFm9HOezZ89yravrfHR0dI5jjo6OiI6OzrfP46RJk7B8+XI8ePAAu3bt0psQ2NraAvj/3yMiIiqZisWkYl30bXXt7+8PIYTWlYnE/7a/1vXiXITiw83NDQDw4sULnWXKli0rl9H3EPbo0aNs5Q21efNm+YFQ17CU9u3by6sLaZZABYDQ0FAsXLgQ9vb2WLhwIeLj43O8NFJTU3Mcy6tjx47h559/RpkyZTB16tQc1876fqWkpCA+Pt6oZTAdHR3lB1rN+6tN1nPGvv/6aL75r1GjRq6/97p+/8PDw3WW1dB1/ujRozli0tyfvvcjMTERMTEx2cobSqFQyIndvXv39JbVJCya3yMiIiqZinVCQKSZO6Dtm1gNQ1ds0az+Yuza/5o17D08PPQuRVv1f9tGZt19NiIiAunp6UhISECNGjXkB+isL4358+fLxzQPi3mlieW///5DmTJlclw763sxZswYODo66p0Mq42mjayrPb3M0JWgjKUZynPv3r18W8/fWJrPY2G8Hy/T/N7om4NDRESvPiYEVKzVqlULgP5vQlu2bCkPjThw4IDWMvfv38fNmzcBAJ06dTIqBs18gKioKL0PnZpeBHMOiSkOOnbsCAD4559/dPbQaP6/VKxYUe9Yf2O1aNECQGbvys6dO83Wbl5o3o+bN2/iwYMHWsto3g9bW1u0bNnSqPbVarW874WPj4/espqEULMHBxERlUxMCKhY02yuFBwcjJSUFK1l7O3t5bH533//fbbVfzQ0K/s4OjqiV69eRsWg2T1bCIEffvhBa5lr167JE1ybN28uH2/btm2uQ1g0/Pz85GPadls2xdChQ/VeO2tvRmBgIIQQRvdODB48GBYWFoiLi8PKlStznA8PD8eWLVsAAB988EGe7udlb7zxhjxUa/r06blOntXX02QuvXv3hqOjI4QQWLBgQY7zMTExWL16NQCgT58+OTZzy/qZ0GbFihVyotGzZ0+d5cLCwuT3o02bNkbdAxFRSbNx40ZIkoSNGzcWdij5ggkBFWstWrSAUqlEamoqLl++rLPc7NmzYW9vj//++w9vv/02QkJCAGROZp09e7b8ADZjxgy4uLjkqN+2bVtIkqR1fknfvn3l9d6//PJLzJs3D8+fP5fb37JlCzp16oT09HRYWVlh/PjxebzrnPTFl9/Cw8PlzQK1zdmpVq0aPvzwQwDAzJkz8f333yM1NRUAcOHCBXTv3h1JSUnw8vLSueSpSqWSlxKOiopCWloagMx5DVmPv5zsSZKE1atXw9raGg8ePEDTpk2xffv2bD0Vjx49wubNm9GxY0dMmTLFHG+JXi4uLpgxYwYAYPXq1Zg9e7bcs3Tnzh28/fbb+O+//2Bvb59j3wQg8+F97ty5uHz5svw+AMDdu3cxceJEfPLJJwCAunXryhviaaPZw8PT0xM1atQw2/0RUcFISksq7BBy0Px7UBz3NdHEnnXVyRJFkMlUKpUAIFQqVWGHUqL17NlTABBffvml3nL79u0TdnZ2AoAAIJydnYWFhYX889ChQ4VardZat02bNgKAqFixotbzZ86cER4eHnJbAISjo6OQJEn+2c7OTmzbts3o+9PU9/Pz01kmt/hMFRYWJl8/MDAw1zK6YkxOThadO3eWy1laWgpHR0f5Zzc3N3H+/HmdcWjuL7dXmzZttNY/ePCgcHNzk8tZWFgINze3bJ8HAGLkyJFGvT+aesZSq9Vi2LBh2eJxdnbO9lnZt2+f1roVK1bMVs/V1VU4ODhku4+mTZuKR48e6Y1h4MCBAoCYNGmS0fETUU5JSUnixo0bIikpKd+vdSz8mLCdayuOhx/P92sZQ/PvQefOnQs7FKNpYvf19dV6PiYmRty8eVPExMQUbGBaGPpZM+Y5lT0EVOyNHj0aAPDLL7/oHU7RrVs3XLlyBaNGjYK3tzeSkpJQqlQpdOzYEdu3b0dgYKDJ6+E3adIEN2/exJw5c9CsWTO4uLggMTER9vb2qFu3LiZPnoyrV6+ib9++JrWfG82KNdpW3CoKrK2t8eeff2Lt2rVo2bIl7O3tkZaWhqpVq2Ly5Mm4fv06GjVqlG/X79ixI0JDQzF//ny0bNkSzs7OiImJgUKhQK1atTBixAjs2bMH3377bb7FkJUkSdiwYQO2b9+Ojh07wsXFBcnJyahYsSJGjRqF4OBgdOvWTWvdr7/+GmPGjEGjRo3g6emJhIQEpKeno0KFCujduze2bNmCf//9V+/qRAkJCdi9ezcAyL03RFR8TD8yHUnpSZh+ZHphh1JiODs7o0aNGgW+V1CBMVOyUiKxh6BoyMjIEJUrVxYAxLFjxwo7nAL38OFD+dviW7duFXY4VAz8+OOPAoBo165dYYdC9MooqB6CoLAgAX/Ir6CwoHy9njG09RD4+voKACIsLEysWrVK1KhRQ1hbW4sKFSoIf39/kZGRka2NjIwMsXbtWtG4cWPh4uIibG1tRcWKFUXPnj21/ht/7Ngx0aNHD+Hm5iasrKxElSpVxPTp00VCQkK2ckFBQXJP9smTJ0WnTp3kntnAwECdPc9BQUFCCCGX0dZb/u+//4pu3boJFxcXYW1tLapXry78/PxyxCCEkHuznz59KoYNGyY8PDyEjY2NaNq0qXyt3LCHgEgLhUKBOXPmAIDWSZqvuiNHjgAAfH19zbpCD72a1Go1Fi1aBACYN29eIUdDRMaaGTQTFpIFAMBCssCsoFmFHJFhPv/8c/j5+aFZs2Zyz76/vz9mzpyZrdy0adMwatQoREdHY9CgQZg4cSJat26N4OBg+d87jdWrV6Nt27Y4efIkevTogQkTJqBcuXKYN28eOnbsKM9Xy+rkyZPyQgoffvgh+vfvj/r162PixIkAgHr16sHPz09+5TY3b8eOHWjTpg2OHj2KXr16YdKkSXBwcEBAQAA6dOigdcGTmJgYtGjRAleuXMH777+Pd999F+fPn0fnzp31LkmdrwxKRUgr9hAUHWq1WjRp0kQAEKdPny7scArU0KFDhbW1tbh//35hh0LFwJYtWwQA0a9fv8IOheiVUhA9BC/3DhS1XgJ9PQQ+Pj4iMjJSPv7s2TNRqlQp4ejoKFJSUuTjrq6uoly5cjm+XVer1eL58+fyz9evXxdKpVI0aNAg23EhhJg/f74AIL7++mv5mKaHAIBYv369zth1zSHQ1kMQGxsrSpUqJaytrUVwcHC2WAcNGiQAiDlz5mRrRxPDRx99lK13ZN26dQKAGD16tNbrZ8UeAiIdJEnCDz/8AD8/P0RFRRV2OAUqMDAQycnJqFChQmGHQsVAWloa/Pz8sHjx4sIOhYiMlLV3QKO49BLMnDlTXpEPANzd3dGzZ0/ExcXh9u3b2cpaWVlBqVRmOyZJElxdXeWff/jhB6Snp2PFihXZjgPAF198AQ8PD/z666854mjQoAGGDx9ujlvCrl27EBMTg+HDh6Nu3brZYl2wYAGUSqXWZUrt7e2xcOFCeR8jILOXX6lU4ty5c2aJzVjK3IsQFQ/169dH/fr1CzsMoiJt8ODBhR0CEZngaPhRnHhwIsfxDJGBfx78g6PhR9HWu23BB2YgzZ49Wb322msAkG1/m/feew+rV69GnTp10L9/f7Rp0wbNmzfPsSfL6dOnAWRu5Hjo0KEcbVtaWuLWrVs5jjdp0iQvt5HNpUuXAGQu/f2y8uXLo3Llyrh9+zbi4uKybUpatWpVODg4ZCuvVCrh6elp9F4/5sKEgIiIiKiI0/QOZIiMHOc0vQTHhx0vhMgMo211Hk0vQEbG/9/TihUrUKlSJWzcuBFz587F3LlzYWNjg/feew9LliyBu7s7gP/fSNLYuVCenp6m3kIOsbGxetv08vLC7du3ERsbmy0h0LVSkVKpzPZeFCQOGSIiIiIqwjS9A9qSASB7L0FxZ2lpic8//xzXr1/Ho0eP8Msvv6BVq1b46aef8P7778vlnJycAGQ+lAshdL5eZury4tpoYnjy5InW85rjmnJFGRMCIiIioiJM29yBlxWXuQTGKFu2LAYOHIgDBw6gatWqOHToEJKSMndobtq0KYD/HzqUVxYWme+vMd/QN2jQAABw9OjRHOcePXqEu3fvolKlStl6B4oqJgRERERERVRuvQMar0IvQUpKCo4cOZLjm/2EhATExcXB0tJSfnD/6KOPoFQq8fHHH+Phw4c52oqJiZHH+BvCxcUFkiQhIiLC4Do9e/aEs7MzAgMDcf36dfm4EALTpk1DWloahg4danB7hYlzCIiIiIiKKH1zB15WHOYS6JOUlIT27dujUqVKaNq0KSpUqID4+Hjs3bsXjx8/xpQpU2BlZQUAqFOnDr777juMHTsW1atXR7du3VC5cmXExsbi3r17OHbsGIYOHYrVq1cbdG0HBwc0btwYx48fx7Bhw1C1alUoFAoMGjRI5yp+Tk5OWLt2LQYOHIimTZuif//+8PDwwOHDh3H+/Hk0adIEn3/+udnen/zEhICIiIioCNK1spAuxWXFIV00y3EePnwY//zzD54+fQoXFxfUqFEDCxcuRP/+/bOVHzVqFOrXr4+lS5fi+PHj2LNnD5ydnVGhQgVMnjwZvr6+Rl1/06ZNmDx5Mnbt2gWVSgUhBJo1a6Z3We9+/frBy8sL8+fPx++//47ExER4e3tj5syZmDJlCmxsbEx6LwqaJLTNuCCDxMbGwtnZGSqVqlhMGCEiIqL8kZycjLCwMPj4+JjtIbBVYCv8++BfCBj+qCZBQssKLYttLwHlztDPmjHPqZxDQERERFTEJKQm4EzEGaOSAQAQEDgdcRqJaYn5FBm9ijhkiIiIiKiIsbeyx6NPHkGVojK6rrO1M+ws7fIhKnpVMSEgIiIiKoI87D3gYe9R2GFQCcAhQ0REREREJRgTAiIiIiKiEowJARERERFRCcaEgIiIiIioBGNCQERERERUgjEhICIiIiIqwZgQEBERERGVYEwIiIiIiIhKMCYERERERMWMEKKwQ6BXCBMCIiIiomLkxo4bWOyxGDd23CjsUOgVwYSAiIiIqJi4seMGtr+3HUnPk7D9ve1MCsgsmBAQERERFQOaZEAzXEgIUaSSgvDwcEiSpPdVv359k9v39vaGjY2N3jI2Njbw9vY2+RollbKwAyAiIiIi/bIlA5rpAwIQyEwK+m7ti1p9ahVqjBqVK1fG4MGDtZ7z8vIq4GjIEEwIiIiIiIowrcmARhFMCqpUqQJ/f//CDoOMwCFDxZlarfc0VyAgIiIq3vQmAxqi6A0fMtTJkyfRvXt3uLq6wsbGBjVq1IC/vz8SExMLO7QShQlBcXLxIvDxx0D9+oCVFWBhkfnf+vUzj1+8KBd9FVcgGDNmDCRJwhdffFHYoRAVWf369YMkSVi8eHFhh0JEeWRQMqBRDJOCHTt2oE2bNjh69Ch69eqFSZMmwcHBAQEBAejQoQNSUlIKO8QSQxL8GtlksbGxcHZ2hkqlgpOTU/5dKDQUGDECOH4cUCqB9PScZTTHW7fGjX5+2D7xBIRaQFJIRaYLMS9CQkLw+uuvw8bGBvfv34ezs7PWclevXkVAQAD+/fdfPH/+HHZ2dqhRowZGjhyJkSNH5jmOY8eOYeHChTh37hxiYmLg5OSE119/HRMnTkTv3r211lGr1fj777+xc+dOnD17FmFhYYiLi4O1tTVee+01tG7dGlOmTEGVKlXyHJ8xrl27hjfeeEP+C3fJkiX45JNPTG5PrVbj66+/xs8//4x79+4hLS0N7u7uaN++PebMmYMKFSporZeamoo//vgDZ8+exaVLl3Djxg1ERkZCCIG3334be/bsMTmmwrZ+/XqsXbsWt27dQmJiItzc3NCiRQvMmjULdevW1Vrn5s2bqFUr99/X0aNHY/Xq1TmO37lzB7Vr14azszPu3r2r83eFiMwrOTkZYWFh8PHxyXXiqyGMSgaykgBJKpx/+8PDw+Hj46N3DkGzZs3QpUsXxMXFoUKFCkhKSsLZs2flvxOFEBg8eDB++eUXzJkzBzNmzJDrent7IyIiItuxl82dOxevvfYawsPDzXpvRYmhnzVjnlPNkhBERkbi6tWriI6ORlpamt6yQ4YMyevlioyCSAhClv6BuKnzMocHZWhJBF4SqXgNF9QNAUiZBwrxLwZzevvtt7F3715MnToV8+fP11rm559/xogRI+QHXDs7OyQnJ0P9v6FVPXv2xO+//w6FwrSOsYULF2L69OnIyMgAANjb2yMxMVEemjVu3DisXLkyR71PPvkE33zzjfyzJEmwt7dHQkKCXNfOzg7fffcdfH19TYrNWGq1Go0bN8bFLL1KeUkIEhMT8dZbb+HMmTMAAAsLC1hZWSEpKQkA4OTkhN27d6Nt27Y56up7AC6uCYFarca7776L3bt3AwAUCgVsbGzkLnBra2usX78e77//fo66Wd8PZ2dnnZ/XESNG6OwF6NevH7Zv346PP/4YK1asMMctEVEuzJkQmJwMaBTSv/2ahECfiRMnYtmyZdi0aROGDBmCsWPH4rvvvstW5uHDh6hUqRIqVqyI0NBQ+bi3tzfu37+faxwVK1ZkQgDjnlPzNGTo6tWraNu2LcqXL49u3bph8ODBGDZsmM7X8OHD83K5Eidk6R+o9unbaJR2Go0yzqIRLup9DcZmnFO/AbUmGQCKZRfiy0JDQ7F//34olUpMnDhRa5kbN25g5MiRSElJweuvv46LFy8iISEBz58/x0cffQQA2L17N6ZOnWpSDH/99Re+/PJLZGRkoE2bNrhz5w7i4+Px6NEj9O3bFwCwatUqrd/Ypqamws7ODv369cPOnTsRGxuLuLg4xMbGYt26dShdujQSExMxcuRInD9/3qT4jLVgwQJcvHgRderUMUt7w4YNw5kzZ6BUKuHv74/4+HgkJibiyJEjqFChAmJjY9GnTx88f/5ca31bW1vUrVsXgwcPxtKlSwu8t0QXzTJ5xpo6daqcDIwbNw7Pnz9HQkICLly4gDp16iAlJQUjR47EjRv6fydPnTqF6OhorS99Q4ImTJgAAPjxxx85DpeomMlzMgAU+r/9nTt3hhBC62vZsmUAgEuXLgGA1i+Kypcvj8qVK+Pu3buIi4vLds7a2lpn20IIWFtb5/ftvZJMTghu376NVq1a4Z9//oEQApaWlihbtiwqVKig81W+fHlzxv5qCwnJ7BkwUE3cQD9sByBy/k8t5knBsmXLoFar0aJFC53LlX3++edITk6Gq6srDh8+jAYNGgAASpUqhVWrVqFPnz4AMh/anzx5YnQMX3zxBdRqNSpVqoS//voLVatWBQCUKVMG27ZtQ9OmTQEA/v7+SH9pSFf//v1x7949bN26Fb169YKDgwMAwMHBASNGjEBQUBCsra2Rnp6Or776yujYjBUSEoJ58+bBwcEBS5cuzXN7ly5dwvbt2wEAkyZNgp+fn/yNRbt27bBv3z5YW1sjOjpaazdvtWrVEB8fj+DgYGzatAmTJ0+Go6NjnuMqLE+ePJF7ivr27YuVK1eiVKlSAICGDRvi0KFDcHV1RXJyMj7//PN8iaFVq1bw8fFBbGws1q9fny/XICLzE0Jg7+i9EOo8JANyY4BQ/6+9Ijg6PDY2FgDg6emp9bzm33tNOcpfJicE/v7+iI2NRZkyZbBnzx4kJCTg4cOHCAsL0/siA40cmesqQhp6kwGNYpoUqNVq/PbbbwCAgQMHai2jUqlw+PBhAMD7778PDw+PHGXmzJkDIHNoy6ZNm4yK4dq1a7hy5QoAYPz48Vq/ffjyyy8BZD4M7t27N9u5Vq1a6fwLDwBq1aqFRo0aAYB8nfzk6+uLxMRE+Pn54bXXXstze+vWrYNarYatra3WB/46deqgc+fOAICdO3fmOG9hYWHyMK6swsPDMWrUKFSpUgX29vawsrJCmTJl8O677+LcuXN5bt9QP/30kzxUSvO5y8rT0xODBg0CABw+fBgqlSpf4ujVqxcAYMOGDfnSPhGZnyRJ6PFDD0gKCTC+c/KlxgBJ8b/2TOjpzG+aISy6vqTTHM/XOZokM/lf4aCgIEiShJ9++gk9evSAhYWFOeMq2S5cyJxAbMCcAYOSAY1imBScOXMGUVFRACA/VL7swIED8rwBzUPQy2rWrImKFSsCAP7++2+jYsg6hl0zPOhl3bp1g62tLQBg3759RrUPQE4yNPMT8ss333yDU6dOoWHDhvjss8/M0uaxY8cAAA0aNNA5gbVr164AMv+CzzpvwVw2bdqEWrVqYd26dbh79y5SU1NhYWGBx48fY+fOnXjzzTezzePIT4cOHQKQOYa1Ro0aWsv07NkTAJCSkoIDBw7kSxwdOnQAkJlkvnjxIl+uQUTmV6tPLfTd2jfzId7U5/hiMH9Q05N/9OjRHOcePXqEu3fvolKlSsW6x7g4MTkhUKlUsLa21jr2i/Jo48bMVYNyYVQyoFHMkoKDBw8CADw8PHRuRX758mX5z40bN9bZlmaYT9YJSoa4du0agMzhR7qGvSmVSjnhuH37tlHtp6SkyPdQrVo1o+oa48GDB/Dz84OVlRUCAwPN1u69e/cAQOfDLwC5BwT4/3Gj5nLo0CEMHz4cSUlJ6Nu3L86fP4+kpCQkJSXh9u3b6N+/P9LT0/H5558jKCjIrNfWJiQkBID+/5eaIWYAEBwcrLNcnz594OjoCEtLS3mFouXLlyM1NTXXOFq3bg0LCwuo1Wr89ddfRtwBERW2PCUFxSAZADK/GHF2dkZgYCCuX78uHxdCYNq0aUhLS8PQoUMLL8ASxuSdisuUKYNnz56ZpaufXvLPP9qXFs3CpGRAowjuaqiLZqhH9erVdZb577//AGSOydf3TUKZMmUAQO5xMNTjx48BQOtQpKw8PT1x69YtPH361Kj2Z8yYIX+DO2bMGKPqGmPYsGGIi4vDZ599pnPJS2O9ePFCHh6jb/hR1lUnIiIizHJtjY8//hjp6en46KOPsGrVqmznqlWrhi1btkCpVOLnn3+Gv78/2rVrZ9brv0zz+dJ83rRxdHSEg4MD4uPj5c+vNjdv3oStrS0sLCwQHR2NkydP4uTJk/jhhx+wb98+vat5ODg4oHz58ggPD8c///yDAQMGmH5TRFTgNEnB9ve2Q8DAOQVFJBkIDQ3Vu1Oxv78/nJycsHbtWgwcOBBNmzZF//794eHhgcOHD+P8+fNo0qRJvs2zopxMfpp/++23kZiYaPZv+wy1aNEieQWQ06dPG1VXrVZj5cqVqFu3LmxtbeHh4YH33ntP/mav0OWy8kiekgGNYtJToBlD6ObmprOMZgWC3JZ5s7OzAwCjV11JSEgwqH3NkCFNeUPs379fXnGhc+fOOvcyyKsffvgBR44cQeXKlTFvnuGT1XOTdSiK5v3VJmuiZs4JYidOnMCtW7egVCoREBCgs5xmD4qzZ8/mmPRtbpoESfN50EUzTOzlFTRsbW3x3nvvYdeuXYiKikJiYiKSk5Nx6dIlDBgwAJIk4ebNm+jcuXOuPQUuLi4AoDfpIKKiy6iegiKSDADA3bt3ERAQoPOl0a9fPwQFBaF169b4/fff8c033yA2NhYzZ87EkSNHzLKfAxnG5B6C6dOnY8uWLZg0aRIOHjxYoMs83bx5E7NmzZLXcjfWmDFjsHbtWtSqVQsff/wxnjx5gt9++w0HDx7EyZMnDdoUKN+o1YDevRwE3sZeSBB5nm+kSQr2jt6Lmu/WLJKTjjQPnJoHm1fJhQsXMGjQIKSnp8Pb2xu//vprvlzn8ePHmDp1KhQKBVavXg0rK6t8uY4++fXZ0kwmV6vVeofoaPaiSE5Oxn///Zdt6FfNmjVzXXnK1dVV6/F69eqZfRiSt7e3PJE+q/r16+PXX3+Fj48P5s+fj5CQECxfvlzvN2iaOR26lnsloqLPoJ6CIpIMeHt7G72iUatWrdCqVSuDyhqyt0BycrJR16dMBiUEDx480Ho8MDAQH3zwgTxBsUmTJrlO/tC1W6mhMjIy4Ovri3r16qFatWrYvHmzUfWDgoKwdu1atGrVCn///becyAwZMgQdO3bE2LFj5UmShUKhACwt9SQFEv5AD/RD5l8MeRqwJWVZ0aAIJgMA5MnC+hJOzWcut78END0D+r7J1sbe3t6g9jXfDGvK63Pp0iV06tQJKpUK5cuXR1BQUL4lPcOHD0dMTAwGDx4sTzQ1l6wx6+t5ydorYM4VIzTffKvVaoMnzsbHx2f7+cWLF7nW1XVe2wpBtra2iIuLkz8Pumg+28ZOmJszZw7Wr1+Pp0+f4o8//tCbEGi+XdNci4iKJ71JQRFJBqh4MyghyG3XuZiYGLlLXh9JkvLcXb9w4UIEBwfj4sWLejfm0WXt2rUAMre2zvqQ2b59e3Tu3BkHDhzAnTt38nVyZ65q1QL0TDS8iVrYhr7oh+1Qm5oUFJO/QEqVKoWIiAi9D2yasdrx8fGIi4vT+YCleXh0d3c3KgbNWsjPnj3TW07zLXPp0qX1lrt06RI6duyI6OhovPbaazh27JjOCdN5tWfPHvz5559wdXXFnDlzEBMTk+181ofj5ORkxMTEQJIknasFvczFxQW2trZISkrSOzcg65LD5ljqVEOzKlOFChUM2r1SG80cEW00ibIx33i5u7sjLi5O7zCduLg4+b3XN9dAGwsLC7z++us4fPgwHj58qLes5v/3q9jDRlTSaE0Kism/5VT0GfQsqW9HOGNeagPX1dfl2rVrCAgIwIwZM1C7dm2T2jh69Cjs7e3RokWLHOc0y1oWag8BALRqlesqQ5qkAJBg9LtajP4C0TzI6EsI6tevL/9Z33rzmjkixu6Cq9nNNyYmRudDb3p6uvxAqm8CtCYZeP78OcqVK4djx47lmnDnhWZFpejoaPj4+MDFxSXbq0mTJnLZ6dOnw8XFBeXKlTPqGpUqVQIA3Lp1S2eZCxcuyH/WLDVnDppkLTIyMt/W8zeWZjWrO3fu6Cxz5swZ+c/16tXLt1g074mxSTARFU3Z5hSg+PxbTkWfQQlBbpuNGfMyVXp6OoYOHYqaNWti6tSpJrWRkJCA//77Dz4+Plr3TdD8Q65rcnFKSgpiY2OzvfLFsGG5rjIEmJgUFKNkAPj/h2t934R26dJF7u3ZvXu31jK3bt2SH9g7duxoVAzvvPOO/OcdO3ZoLfPnn3/KQ0S6d++utcylS5fQoUMHORk4fvy4/DBdnLVp0wZA5v29PEFWQ7PWvqenJxo2bGi2a2tWDEpPT8ePP/5otnbzQjMs68GDBzqTAs3eFtbW1ujSpYtR7WdkZODq1asAcu9tiYyMBJA5T4KIXg2apMDWzbbY/FtORZ9BCUHFihXN9jLVV199heDgYGzYsAGWlpYmtaH5tkzXcAjN2GZd3zTOnz8fzs7O8kvXmvR51rAh0Lo1YJH7iC6jkoJilgwAwFtvvQUg85tuXWPUnZ2d0b59ewDA5s2btS4r6ufnByBzfPcHH3xgVAx16tSRl+n89ttvtY7Hnj9/PoDMB94ePXrkOK9JBjTDhAoqGfjkk0/09trdyLKi1ZIlSyCEyDHGPjcjR46EQqFAUlKS1p15b968KScE5l5FqW3btnIiP2/evFyXNNU3PMhchgwZAltbWwghMHPmzBznnz17hp9//hlA5lDFl/8+yq0n1d/fX17aVttnTePGjRvy32W6NvUjouKpVp9a+PzZ58Xm33Iq+orFJgLBwcGYO3cuPvvsM7N+u2isadOmQaVSya/cxu/myfr1mROMDWBQUlAMkwEA6NSpEywsLJCeno5///1XZ7nFixfDxsYG0dHR6NChg7zRl0qlwoQJE7Bt2zYAwPjx4+Hp6ZmjfsOGDSFJkjwE5WWLFi2CQqHA3bt30bVrV3kozuPHj9G/f3+cOnUKQObDmvKl4V5XrlyR5wyUL1/epGQgt/jy082bN+UlfsePH5/jfIMGDeQdnL/55hvMnTtXnoB99OhRdO3aFSkpKfI8Bm2ioqIQGRkpv9L+N6k+JSUl2/GXkz2FQoHvvvsOlpaWePr0KRo3bow1a9Zk66kIDQ3FihUr0KRJE3z00UdmeU/08fT0lN+nbdu2YcKECfKDedbE0MbGRus8qIYNG2LSpEn4559/siWfV69exeDBg+VlYytXroxJkybpjEOz+6eLi0u2jeGI6NVQVBcDoWJKmEiSJFG2bFmDy3t7ewsLCwuTrlWvXj1Ro0YNkZycnO24r6+vACBOnTplUDvx8fECgKhTp47W83v37hUAxOeff25QeyqVSgAQKpXKoPLGurNkjwCEwa+auC5mIUDMgr/wz/qS/EWAIkBc3349X+LMb61btxYAxOjRo/WW27x5s7C2thbInGol7O3thUKhkH9+5513REZGhta6DRo0EACEp6enzvYXLFggLCws5PYcHByEJEnyz+PGjdNar2fPnnIZOzs74eLiovdlanymuHHjhhzbkiVLci2j6x4TEhJE06ZN5XJKpVLY2trKPzs5OYmgoCCdcWjuL7dXgwYNtNbfunWrcHJyksspFArh5OSU7fMAQPTu3duo90dTz1gZGRnZ/r8rFAphb28v/2xtbS02b96sta6np2e2eo6OjtneSwCiVq1a4u7du3pj6Ny5swAgPvjgA6PjJyLjJSUliRs3boikpKTCDoVecYZ+1ox5TjV5H4L/JRP5Wl4j+H8r7ujaoKJ58+YAgJ07d6JXr14627G3t0eZMmUQFhaGjIyMHPMINHMHNEMQClvVT97GHfyBuKnzMvcnyMh9XkGk4jVcUDeEvINJMe0ZyGrMmDE4fvw4du/eje+++07n7tjvv/8+6tatC39/f5w8eRJRUVFwcHBAjRo1MGrUKINWwtJnypQpaNasGRYsWIDz588jJiYGpUqVQt26dTFx4kSdw2GyDgFJTEw0emM0APIQEXPtMGxudnZ2OHnyJBYvXoxffvkF9+7dQ2pqKsqVK4f27dtjzpw5eV5yWJ9+/fqhbdu2WLRoEQ4ePIiwsDDEx8fD2toaFStWRMOGDdGrVy/06dMn32LISqFQYNeuXVi3bh3Wrl2L27dvIzExEZ6enmjRogX8/Px0/r+cPXs2Dh06hCtXriAqKgqxsbGQJAmlS5dGrVq18N5772HUqFE5eqKyUqlUcg/BhAkT8uMWiYjoFSIJE5/SFQoFvLy85ElruSlXrhyePn0qDwUwhq4HuePHjyMkJATvvPMOPDw8MH78+GwrzmgzcOBAbNmyBceOHUPr1q2znevatSsOHDiA27dvG7TsaGxsLJydnaFSqcy6tnoOoaHAiBHA8eOZqw9pm3CsOd6mDW70nYXtE09AqAUkRfFOBoDMSZQVKlRAZGQk9uzZg7fffruwQypQISEhqFatGhQKBc6dO1eow+aoePjmm2/wySefoGHDhtlWeCKi/JOcnIywsDD4+Phwh13KV4Z+1ox6TjW1u0KSJFGmTBmDyr548UIolUrh7u5u6uW00jdk6NmzZ+LmzZvi2bNn2Y4fOXJEABCtWrUSKSkp8vFDhw4JSZJE69atDb5+fg8ZyuHCBSHGjxeifn0hLC0zxwlZWmb+PH585vn/ub79uljotrDYDhN62cqVKwUA0bx588IOpcAtXbpUABA9e/Ys7FCoGEhPTxfe3t4CgDhw4EBhh0NUYnDIEBWUQh0ydOXKFXmipkZSUhJ++uknfckGYmJisH37dqjV6gL9ZnPlypUICAiAn58f/P395ePt2rXDyJEjsW7dOjRo0ADdu3fHkydP8Ntvv8HJyQnff/99gcVotIYNM18aarXOice1+tRCzXdrvjKTjsaOHYtVq1bh1KlT+Pvvv41eOrQ4O3r0KCwtLbFo0aLCDoWKgTVr1iA8PBwdOnTg6kJERGQQgxOCnTt3Yvbs2dmOxcbGYtiwYbnWFUJAkiR88sknxkeYD3744QfUrVsXP/zwA1asWAEHBwe8/fbbmDdvXuHuUGysXFYhelWSASBziNoPP/yA3377Td4RuKTQtbcCkTZpaWkYN26c1hWhiIiItDF4DsHy5cuxbNky+ef79+/DwsJC78Y4CoUCTk5OqFOnDj788EO0atUqzwEXJQU2h4CIiIiKNM4hoIKSH3MIDO4hmDhxIiZOnCj/rFAo4OHhkafdh4mIiIhIh+RnQJr2zVL1snQGbDzMHw+9skxedtTPzw8ODg7mjIWIiIiIACA9AdhZDhDGr84IyRLoFwMo7cwelqGGDBmCTZs2wdPTExEREXqXSgYyh5dXqlQJ4eHh6NOnD7Zv3661XHh4OHx8fPS2Va9evRzzXkm/PCUERERERJQPlPaAe1Pg2b/I3JPQUBLg3qxQk4HY2Fjs2LEDkiThyZMn2LdvH3r27Km3zuHDhxEeHg5JkrBnzx48e/YMHh66ezkqV66MwYMHaz3n5eWVp/hLojxtTEZERERE+aTuHOBwOyMrCaDu7NyL5aNff/0ViYmJ+Oyzz7BkyRKsX78+14Rg/fr1AIBPP/0UX3/9NTZt2qR3MZoqVapkW0WS8sbkhGD48OFGlbexsUGpUqVQu3ZttG/fntkbERERkT6ebQGPlkDUKUBk5F5esgDc38ysV4jWr18PKysrTJs2DadPn8b+/fvx33//oUyZMlrLv3jxAjt37kSjRo0wa9YsfPfdd1i/fn2RWZ2yJDA5Idi4caNRy1pqlh4FAKVSCV9fXyxdupTzEIiIiIh0MaaXQGQUeu/A1atXce7cOfTu3Ruurq4YMmQITpw4gR9//BFTp07VWmfz5s1ISUnBkCFD4OjoiF69euGXX37B6dOn0axZswK+g5LJ5IRgyJAh8jivFy9ewM7ODo0aNUK5cuUghEBkZCQuXLiAxMREuLq6okePHoiJicHFixcRERGB9evXIyQkBIcOHYKFhYU574mIiIjo1WBoL0ER6h0AgA8++AAA8N5772HChAnYsGGDzoRgw4YNUCqVGDBgAADA19cXv/zyC9avX68zIQgNDdU5ZKhZs2bo0qVLHu+khMnL1smDBg0SCoVCBAQEiLi4uBzn4+PjxezZs4WFhYXw9fWVj//444/C2tpaKBQKsXHjxryEUKiM2RKaiIiIXl1JSUnixo0bIikpyfyNPw4S4mfk/nocZP5rGyElJUW4ubkJFxcXkZKSIh/v37+/ACCOHTuWo8758+cFANG9e3f5WEZGhihbtqxwdHQU8fHx2cqHhYUJZM6y1vmaOHFivt1jUWDoZ82Y51T9W93qsXbtWmzZsgVfffUVZs2apXXoj729PWbOnIl58+Zh06ZN2LhxI4DM3gV/f38IIfDrr7+aGgIRERHRq0/TSyDpGFEhWQAerQq9d2DXrl14/vw5+vfvDysrK/n4kCFDAGT2BLzs5R4FIHOvq/fffx9xcXHYtm2b1mt17twZQgitr6wb6ZJhTE4I1q9fD4VCgXHjxuVadty4cVAoFFi7dq18TDMpmevEEhEREeWi7hzdQ4aKwNwB4P8f+LM+3AOZD+9eXl7Ytm0bYmNj5ePJycn49ddf4eTkhHfeeSdbHV9fXwD/nzBQ/jI5Ibh16xacnZ0NmhTs4OAAJycnXL9+XT5WunRpODs7IyYmxtQQiIiIiEoGXb0ERaR34OHDh/j7778BAC1atIAkSfJLqVTi8ePHSExMxJYtW+Q6O3bsQExMDGJjY2FnZ5etTp06dQAAJ06cwO3btwvlnkoSkycVq9VqxMTE4MWLF3BxcdFb9sWLF1CpVLCzy75JRlpaGlcZIiIiIjKEthWHikjvQGBgINRqNVq2bInq1avnOJ+amopNmzZh/fr1+PDDDwH8/7f//fr1g5OTU4469+/fx6FDh7BhwwYsXLgwf2+ghDM5IahTpw7OnDmD+fPnY9GiRXrLLliwAGq1GrVr15aPPX/+HImJiahQoYKpIRARERGVHC+vOFREVhYSQiAwMBCSJOGnn36Cj4+P1nLXrl3D2bNnce3aNdjZ2eHo0aPw8fHBb7/9pnUp+6ioKJQrVw4//vgj5s2bB6WS++nmF5Pf2VGjRuH06dNYsmQJVCoVvvzyS1SsWDFbmQcPHuCrr77C2rVrIUmSnBECwNGjRwEADRo0MDUEIiIiopIlay9BEekdOHz4MMLDw9GuXTudyQAADBs2DJcuXcL69ethb28PIQSGDh2qc18rd3d39OjRA7///jv27duXbbdjfcuOAuAuxkaShBDC1MoDBgzA1q1b5f+RFSpUQNmyZSFJEiIjI3H//n0AmZljv3798Ntvv8l1Bw0ahD///BPffvstBg8enMfbKByxsbFwdnaGSqXS2tVFREREJUNycjLCwsLg4+MDGxub/L3Y362AZycy5w50PJ6/1zLAwIEDsWXLFmzatEnvM93z589RtmxZODk5QalU4unTpwgLC9M7WmTv3r14++238fbbb2PPnj0IDw/Xm3Ro5OHxtsgz9LNmzHNqnhICtVqNRYsWYcGCBdlmjWfl5OSEKVOm4IsvvnjlNiBjQkBERERAAScET48DQV2BdgeA0q3y91pU5BS5hEAjKSkJf/31Fy5evIhnz55BCIHSpUujYcOG6NSpU47JxK8KJgREREQEFHBCAADpSYDSNv+vQ0VOfiQEZpmdYWtri169eqFXr17maI6IiIiI9GEyQGZk8j4ERERERERU/DEhICIiIiIqwfI8ZOjo0aP49ddfceXKFURHRyMtLU1nWUmScPfu3bxekoiIiIiIzMTkhEAIgeHDh+Onn36Sf86NrnVmiYiIiIiocJicEHz77bf48ccfAQCNGjXCO++8g7Jly3IXOSIiIiKiYsTkp3fNFtUjR47EDz/8YM6YiIiIiIiogJg8qfjOnTsAgAULFpgtGCIiIiIiKlgm9xDY2NjAxsYGLi4u5oyHiIiIiIgKkMk9BK+//jpiY2MRHx9vzniIiIiIiKgAmZwQjB8/HhkZGdiwYYM54yEiIiIiogJkckLQt29fjBs3DlOmTMGmTZvMGRMRERERERUQk+cQDB8+HABgZ2eHoUOHYubMmWjcuDEcHR111pEkCevXrzf1kkRERESUi/TERCjt7Ar8uuHh4fDx8QEAlC1bFg8ePICFhUWOclevXkXdunUBANWrV8etW7cAABs3bsSwYcP0XmPixIlYtmxZnuLr3LkzDhw4oLXM6dOn0bx5c/j6+mLjxo0mXac4Mjkh2LhxIyRJkjcke/DgAR48eKC1rKYcEwIiIiKi/PHk7Flc+fZbRF25gt5BQbBxdS2UOJRKJSIjI/HXX3+hW7duOc6vX78eSqUS6enpWuu3b98eLVu21HquWbNmZo2VMpmcEAwZMoQ7DxMREREVMk0i8OziRUCSACGQFhdXaAnBm2++ieDgYGzYsCFHQpCamoqff/4Z3bp1w549e7TW79ChA6ZOnVoQodL/5KmHgIiIiIgKR9ZEQNIMzfnfyI3CZGtri/79+2Pjxo2IioqCu7u7fG7Pnj2IiorCsGHDdCYExvrxxx/x/fff49q1awCAOnXqYOzYsfD19TVL+yWByZOKiYiIiKjgPTl7Fn9/8AEODxuGqOBgAIDIyCjkqLIbPny43BuQ1YYNG1C6dGn06NHDLNeZPHkyhg4dioiICIwYMQIjR47Eo0ePMHToUHzyySdmuUZJYHIPAREREREVHG09AkUtEdBo2rQpateujQ0bNmDixIkAgEePHuHgwYOYNGkSlErdj6CHDh1CcnKy1nMDBgxAjRo1AAD//PMPli1bhpo1a+LUqVNwdnYGAAQEBKBZs2b45ptv8O677+aYjxAaGgp/f3+t7UdERBh7q68EsyQEe/bswV9//YX79+8jKSkJhw8fls8lJCQgODgYkiShefPm5rgcERERUYlRnBKBrIYNG4bPPvsMFy5cQKNGjbBx40ZkZGTIK1Xqcvjw4WzPklnVr19fTgg0w9f9/f3lZAAAnJ2d4efnh4EDB2Ljxo05EoK7d+8iICAgD3f26snTkKGHDx+icePG6N27N1avXo39+/fj6NGj2cpYW1tj4MCBaNmyJa5evZqXyxERERGVGE/Pny/yQ4P0+eCDD2BpaSlvYrtx40Y0bdoUtWrV0ltv/vz5EEJoffXq1Usud+nSJQBA27Ztc7ShOXb58uUc5zp37qyz/VOnTpl0r8WdyQlBYmIiOnXqhAsXLqBcuXIYN24c7O3tc5RTKpUYOXIkhBDYvXt3noIlIiIiKgnSk5MRNHp05spBKF6JgEbp0qXRrVs3/Prrr/jrr78QGhqa6z4DxoiNjYVCoYCHh0eOc56enlAoFFCpVGa73qvM5IRg1apVuH37Nho2bIibN29ixYoVcHBw0Fq2Z8+eAICDBw+aejkiIiKiEkNpY4N2a9bAo2FDAPj/VYSKmeHDh+PFixcYMWIEbG1tMXDgQLO17eTkBLVajWfPnuU49/TpU6jVajg5OZnteq8ykxOC7du3Q5IkLF26VGvPQFZ16tSBUqnEnTt3TL0cERERUYlSulEjdNy0Ce0DA+Ferx6A4pcYdOvWDV5eXnj06BH69Olj1gf0Bg0aAECO4eoAcOzYMQCZcw4odyYnBLdv34aFhQVatGiR+0UUCjg7O+PFixemXo6IiIioRPJs0qTYJgZKpRJ79uzBzp07MW/ePLO2rdlnICAgALGxsfLx2NhYedIw9yIwjMmrDKWkpMDW1hYWBn4gExISYG1tberliIiIiEo0TWLw8qpDRX1+QePGjdG4cWODy+tbdtTb2xtDhw4FALRu3Roff/wxvv32W9SpUwd9+vSBEAK///47Hj58iAkTJqB169bmuIVXnskJQenSpREREYGYmBiUKlVKb9ng4GAkJyfLy0QRERERkWmKa2JgKH3LjrZp00ZOCABgxYoVaNCgAb7//nusWbMGAFC7dm0EBASYdQLzq87khODNN9/E1q1bsXXrVnz44Yd6y86bNw+SJKFNmzamXo6IiIiIstCWGECSACEKJR5vb28II679ctmhQ4dme9g31LBhwwx6+DckvmbNmhl1D68Kk+cQjBkzBkII+Pv748aNG1rLJCYmYty4cdi+fbtch4iIiIjMJ+scA48GDSAplbB0dCzssKgYMTkhaNOmDUaMGIHHjx+jadOmGDBgABISEgAAixcvxpAhQ1C+fHmsXr0aADBp0iTU+99EGGPFxMRgwoQJaN68Oby8vGBtbY1y5crhrbfewo4dO4zK5CIiIjB69GhUqFABVlZWKFu2LIYNG4aHDx+aFBsVnBkzZkCSJCxatKiwQyEqssaMGQNJkuSNgIio5NAkBv1OnYKNq2thh0PFiciD9PR0MXHiRKFQKIQkSUKSJKFQKOSX5udPP/1UqNVqk68TEhIi7O3tRfv27cXo0aPFtGnTxIgRI0Tp0qUFADFq1CiD2gkNDZXrdOzYUXz22WeiZ8+eQpIkUbp0aREaGmpUXCqVSgAQKpXKlNsiIzx8+FDY2toKDw8PER8fr7NcaGio+PDDD4W3t7ewtrYWHh4eolOnTmL79u1miePChQvi/fffF+XKlRNWVlbCy8tL9OrVSxw+fFhnndTUVLFmzRrx4YcfiiZNmojXXntN2NjYCFtbW1GpUiUxcOBA8ffff5slPm1iYmLEypUrxdChQ0WDBg1E2bJlhZWVlbC3txfVq1cXI0aMEGfPns3zdW7evCnGjh0ratasKezs7ISlpaXw8vISXbp0ET/99JPIyMjQW//q1ati+PDhwsfHR1hbWwsXFxfRrFkzsXz5cpGamprn+ArDjh07RKdOnYSHh4ewtrYW3t7e4sMPPxQhISE664SFhQkAub6mT5+utf79+/eFlZWVKFu2rN7fFSIyr6SkJHHjxg2RlJRU2KHQK87Qz5oxz6l5Sgg0rl27JiZNmiQaN24sypUrJ7y8vET9+vXFuHHjxOXLl/Pcfnp6ukhLS8txPDY2VtSqVUsAENeuXcu1ne7duwsAYvny5dmOb926VQAQnTt3NiouJgQFZ8iQIQKAWLx4sc4y+/btE3Z2dvIDk5OTk1AoFPLPw4YNy1NiunbtWqFUKuX2nJ2dhSRJ8s9+fn5a6z179izbg5wkScLFxUVYWFhkO+7r66v1c55X586dy3YdhUIhXFxcsr03kiSJadOmmXyNwMBAYWlpKbenVCqFo6Njtuu2bt1axMbGaq3//fff53hvbWxs5J8bNmwooqOjTY6voKnVajFs2LBs77mTk5P8s52dndi3b5/WulkTAnd3d+Hp6an1tWDBAp3XHz16tAAgAgIC8usWieglTAiooBTZhKAwTZ48WQAQu3bt0lsuKSlJKJVK4enpqfWhsH79+gKAuHv3rsHXLsoJQVpCQmGHYDYRERHCwsJCWFlZiefPn2stc+/ePWFvby8AiBYtWojbt28LIYSIi4sTs2bNkh+wFi5caFIMJ0+elB/ge/XqJR4+fCiEECIqKkp++AIgfvvttxx1Y2Njxccffyx+++03ER4eLn/bnZGRIa5evSoGDBgg158/f75J8ekTGhoqPv/8c7Fr1y7x6NEjkZ6eLoQQIi0tTZw+fVp07NhRvv6vv/5qdPu3bt2Sk4G6deuK48ePy9d49uyZmD59utz+hAkTctT/66+/5MSqffv24ubNm0KIzC8C9u3bJ8qUKSP36hU0TdzGWrhwYbZEMS4uTgiR+V69+eabAoCwt7cX9+7dy1E3a0IQFhZmUtyXLl0SAISbm5tITk42qQ0iMg4TAiooTAhekpSUJOrVqyckSRJ37tzRWzYiIkIAEE2aNNF6vnfv3gKAWLduncHXL4oJweMzZ8TBwYPFL3XriiQdD8/FjZ+fn/wgrsvgwYMFAOHl5SVevHiR4/yHH34o9xqY8k1zy5YtBQDx+uuvax2+0rlzZwFAVKxYUX4YNpRarZYfEqtUqWJ0bHmVnJwsvL29BQDRoUMHo+v7+/vn+gCb9f/Py9544w0BQJQrV04kaElkg4KC5Pb37t1rdHx5YUpCEB0dLfeOjB49Wut5Ly8vAUAMHjw4x3lzJARCCFGnTh0BQGzevNnkNojIcEwIqKDkR0Jg8qTiwhATEwN/f3/MmjULY8aMQbVq1RAcHIxZs2ahatWqeuu6uLjAwsIC9+/f1zoJOSwsDABw584dnW2kpKQgNjY226uoeHL2LP7+4AMcHjYMzy5dgkhPR1pcXGGHlWdCCKxfvx4AMGjQIK1lEhISsGPHDgDA2LFjte6LMW3aNACZuxfu2rXLqBju3buHEydOAAA+++wzWFpa6mz//v37OH78uFHtS5KEpk2bAsic9F7QrK2t5e3fTbn+f//9BwBwc3ODt7e31jJNmjQBAMTHx2c7/uTJE5w/fx4A8NFHH8HOzi5H3bZt28ob2gQGBuqM4/Hjx5g6dSrq1asHZ2dn2NjYoFKlShg5cqTOldDyw86dOxH3v989zeciKxcXF3nFtR07dsiLMZjbwIEDAUBel5uICoa2Zwwic8qPz5hB+xAY+4CjT152jIuJiZG3ogYAS0tLLF68GJ9++mmude3s7NCmTRscOXIE3333HcaNGyef+/3333H58mX5GrrMnz8/2/WLgpc3JQFQaOsP54dr167JD6mtWrXSWubEiRNISkoCAHTt2lVrGW9vb9SsWRM3b97EwYMHjdqs5O+//5b/3KVLF61lWrZsCUdHR8TFxeHgwYNo166dwe2r1WqcPHkSAFC5cmWD65lLUlISLly4YPL1K1WqBAB4/vw5wsPDtSYFZ8+eBQC88cYb2Y7fv39f/nOtWrV0XqNmzZo4d+4c/vrrLwghIElStvN79+7FwIED5YTD0tISVlZWCAsLw/r167Fp0yasXbsWQ4YMMfr+jKX5vNSqVQsVK1bUWqZr167w9/dHUlISTpw4gc6dO5s9Ds3ftf/++y/i4uLgyCUIifKVxf/+DU5LS4OtrW0hR0OvsrS0NAD//5kzC0O6Jl5ePcjUl4WFhSGXy1V6eroICwsT8+fPF1ZWVqJ3794GTca8fPmycHBwkCcQf/7556J3795CoVCIunXrCgBi7NixOusnJycLlUolvx4+fFhoQ4Y0Q4N+rlVL/PL66+LnWrVyvGLDwws8LnNbuXKlACDKly+vs8zXX38tD7HQNuREo1+/fvKwH2OMHz9eABClS5fWW65x48YCgHj77bcNavf58+fin3/+Ee+8844c/4YNG4yKzVRqtVo8efJEHDhwQLRq1Uq+vr7VknSJjIyU52/UrVtX/PPPP9nmEMyYMUOeSPvyakZnzpyRr61vJaj3339fLvfyuPszZ84IKysreYjOzZs35evfv39ffPTRR/JE53Pnzhl1b5prGkMzVOe9997TWSYhIUFue8mSJdnOZR0y1KRJE+Hi4iKv2NStWzfx008/GfT3XWJiojxR+88//zTqHojINHfv3hUPHjzI0wIWRPqo1Wrx4MEDg+a8GjNkyOCdioUZvnU2RxtAZkbk7e2NqVOnwsLCAl988QXWrl2LsWPH6q1Xr149nDt3Dn5+fggKCkJQUBCqVKmCH374ATExMfj888/h4eGhs761tTWsra3Ncg+m0tYj8KpsVa7NmTNnAEDvHhaRkZEAModiaBtyolGuXLls5Q2lKa+pr6/9c+fO6W1/wYIFWoeRODg4YM6cOfm+zfqYMWPwww8/5Dju5uaGVatW4a233jK6zTJlymDv3r3o378/rly5glatWkGpVMLOzg6xsbGwtLREr169MHv2bLz++uvZ6mbtTbh27Rr69Omj9RrXr1+X/xwZGQkfHx/55/HjxyM1NRUzZ87E7Nmzs9WrUKECVq1aBaVSiRUrVmDu3LlGDxkzliGfFzs7O5QqVQoxMTF6Py9nz56Fo6MjLC0t8fjxY+zfvx/79+/HypUrsWvXLpQpU0ZnXVtbW1SvXh3Xr1/HqVOndPZuEZH5uLu749GjR4iIiICzszMsLS1z9GgSmUIIgbS0NKhUKsTHx+f6TGIsgxICtVpt1ouaU6dOnfDFF1/g6NGjuSYEAFCjRg389ttvOY5rtsp+eUhDUVHSEgENzcOSvkRNM15bXzKQ9XyckXMrzNm+g4MDPD09oVarERUVBSEE7OzsMGfOHIwYMcKouEzh7OwMT09PpKen4/nz5wAyk4ElS5agV69eJrfbtm1bHDt2DIMHD8aFCxeQnp4uz7HJyMhAfHw8oqKictQrXbo03njjDZw/fx6rVq3CpEmT4OzsnK3Mvn375CF9ALLN3QkODsa5c+dgaWmpd+jgkCFDsGLFChw6dAgZGRnm7WZ9iTGfl5iYmByfFxsbG4wdOxbvvfceGjZsCCcnJwBASEgIli9fju+++w5nz55F9+7dcfbsWSiVuv8ad3d3B2B8EkxEptH8vkZFReHRo0eFHA29ijSb82o+a+ZicA9BUaX5h07fP4q5iYuLwx9//AFXV1d07NjRXKGZxdPz5xG8fHmJSwQ0nj17BgBwfUV2XBw/fjzGjx8PIHOS+qVLl+Dn54fJkyfju+++wx9//IHq1avn2/UXLlyIhQsXAgASExNx8uRJTJ8+HUOHDsV3332H3bt3w8vLy+h2Fy1ahGnTpsHT0xMbN27EW2+9BRcXF9y+fRtff/01tmzZgqCgIAQGBuKDDz7IVnf27Nno3r07nj17hg4dOmDJkiVo0qQJkpKSsHv3bkyePBmWlpbymEmF4v/XQtBM9lar1Xrft4z//c4kJCTg+fPnKF26tHyucePGue5Urus9efPNN/H777/rrWssLy8vfPfddzmOV61aFStXrkSlSpXw6aef4tKlS/jpp58wfPhwnW1pfm80v0dElP+cnJzg5OSEtLQ0+e8eInOwsLDQurCJORSLhODy5cvw8fHJ8c1hdHQ0vvzySwDZJ5NGRUUhKioK7u7u8jdkQObkSUtLy2zJQ0pKCkaMGIHo6GgsX74cNjY2+Xw3hktPTkbQ6NHISE4GULISAY3k/927vqFamsmSiYmJetvSnDd2cmV+tW9tbY1mzZrhwIED6NWrF/bs2YPBgwfj7NmzBdLFbGdnhw4dOqB169Z48803cfbsWYwfPx7bt283qp1t27ZhypQpsLa2xuHDh1GzZk35XKNGjfDrr7/C0tISmzZtwoQJE9CtWze4ubnJZbp27Ypvv/0WEydOxPnz59GmTZts7Ts6OmLu3LmYMmUKgMyhYRqaLwQyMjLw5MkTg+J9+f/js2fPcq2r63x0dHSOY46OjoiOjs63z+OkSZOwfPlyPHjwALt27dKbEGgmNmp+j4io4FhaWubbwxuRuRWLZUc3btyIcuXK4e2338b48eMxZcoUDBgwABUrVsTly5fRp0+fbEtSrly5EjVr1sTKlSuztXPhwgWULVsW77//PqZOnYqPPvoI1apVw7Zt2zBq1Ch8/PHHBX1reiltbNBuzRp4NGwIAP+/ilAJonlwfPHihc4yZcuWlcvoewjTdN9qyhtKUz637l9T25ckCZMmTQIAnD9/HpcuXTKqfl5ZWVnJq27t2LFD60OuPl9//TUAoHv37tmSgaw+//xzAJmreB0+fDjH+XHjxuHq1asYP348GjRogPLly6Nu3br4+OOPcfXqVZQvXx5A5nuVdYlhzbdvNWrUgMjcVyXX18urIIWHh+ssq6Hr/NGjR3PciyGfl8TERHlFM2M/LwqFQl7G9d69e3rLav5fZk3AiIiIXlYsegj69u0LlUqF06dP4/jx40hMTISrqytatmyJIUOGYMCAAQZ9o1qhQgW0bdsW//zzD548eQI7Ozs0bNgQS5cu1TmZsbCVbtQIHTdtyjGHoKT0FmjmDuh7SK1Tp4785+vXr8tr1r/s2rVrAIDatWsbFYOm/adPn+LZs2da5zNkZGTg1q1bJrUPZJ+AGhoaiob/SwILysvX1zxwGkKzxr++JUuzPsRr9vx4Wc2aNfHtt99qPbdgwQK5TNYeAs1Qnnv37iEhIQH29vYGx51f6tSpg2vXrsmfN22ynjPl82Ioze+Nvjk4RERExaKHoGXLlggMDMTNmzehUqmQlpaGJ0+e4M8//8TAgQNzJAP+/v4QQsDf3z/b8QoVKmDr1q148OABUlJS8OLFCxw+fLjIJgNZeTZpgo6bNqF9YCDc/7fiTknoMdCsTa/vm9CWLVvKQyMOHDigtcz9+/dx8+ZNAJkT0Y2RdV6JrvY1a72b0j6Q/f4KY734vFxfM6Y/654CL8s65MbY9uPj4+WFAHx9fbOda9GiBQAgNTUVO3fuNKrd/KL5vNy8eRMPHjzQWkbzObK1tUXLli2Nal+tVsv7OmRdbUkbTfKlq+eGiIgIKCYJAf2/kpYYaDZXCg4ORkpKitYy9vb2clL3/fffQ6VS5SijmUjr6Oho9Go6lSpVkh/alixZIk9uzUrzDXbFihVzbL6Xnp6ut/309HQsXrwYQObwnebNmxsVX25yu358fLz8zbyXl5fRk5o1vRl//vmnzqTg+++/l/9s7P1NnDgRL168gJeXF0aNGpXt3BtvvCHvsjx9+vRcJ88aOxzKFL1794ajoyOEEPLnIquYmBisXr0aANCnT58cvRq5Lc+8YsUKOdHo2bOnznJhYWHy+/HyvAwiIqJsDNwHgbQwZsOH/KJvg7JXYWMylUolb650+vRpneXu3bsnb47VqlUrcefOHSGEEPHx8SIgIEBIkiQAiIULF2qt36ZNGwFAVKxYUev5kydPCgsLCwFAvPvuuyIiIkIIkbm52NixY+WNpH777bccdceMGSPGjBkjgoKCRFxcnHw8OTlZHD58WL42APHll1+aFJ8+PXv2FJ9//rk4ffq0SEpKko/Hx8eL3bt3y5vyARBr1qzJUT/rRll+fn45zu/YsUM+X716dXHgwAGRnJwshBAiIiJCTJw4UX7/27Vrl6N+fHy8mDRpkjhx4oSIj48XQmRuvHLu3DnRs2dPeVOxffv2ab2/M2fOCGtrawFA+Pj4iG3btmXboC4iIkJs2rRJdOjQQYwcOdKo905zX8ZauHChACAkSRIBAQHyfd2+fVu0bNlSABD29vY5NlkTQohWrVqJOXPmiEuXLonU1FT5eGhoqJgwYYL8XtatWzfb+Zf9+uuvAoDw9PQ0On4iIir+jHlOZUKQB0UhIdDImhj8XLv2K5MQCCHkh0JdD8sa+/btE3Z2dvJDnLOzs/wQD0AMHTpU5+6Rhjxwr127Vk5OAIhSpUrJD2e6HpaFEMLX11cuI0mScHJyEm5ubtlikyRJTJw4UWRkZJgcny5ZEw6FQiFKlSolXFxcssVuZWWlM1nKLSEQQoiZM2dma0+hUMi7gmter7/+unj8+HGOui9evMhWrlSpUvLOw5qfd+3apfceDx48KNzc3OQ6FhYWws3NLdvnAUCBJQRqtVoMGzYsWzzOzs7yz3Z2djoTnIoVK2ar5+rqmuO9bNq0qXj06JHeGAYOHCgAiEmTJhkdPxERFX9MCApIUUoINDSJwS9164qk588LOxyz2L9/vwAgvL29c90OPjQ0VIwaNUp4e3sLKysr4ebmJjp27Ci2b9+ut56hD9wXLlwQgwYNEuXKlRNWVlbC09NT9OrVSxw+fFhnnZs3b4pFixaJ7t27i6pVqwpnZ2ehVCqFi4uLaNSokZg4caK4fPmy3utWqVJFABD9+/fXW06b8+fPi9mzZ4sOHTqISpUqCQcHB6FUKoWbm5to3ry5+PLLL0VoaKjO+oYkBEIIcfr0aTFs2DBRrVo1YWdnJ5RKpfDw8BDt27cX33//vdxr8LLU1FTx1VdfiU6dOony5csLa2trUapUKdGwYUMxc+ZM8fTpU4Pu88WLF2L+/PmiZcuWwtXVVVhYWAgHBwdRq1YtMWLECLFnz55sPSSGMDUh0Ni+fbvo2LGjcHd3F9bW1qJixYpi1KhRIiQkRGedbdu2iTFjxohGjRqJsmXLCmtra2FjYyMqVKggevfuLbZs2SLS09P1Xjc+Pl5Ohm7cuGFy/EREVHwZ85wqCZHLgFXSKTY2Fs7OzlCpVGbfMS6v0hMTocxlp9TiQq1Wo1q1arh79y6OHTuWY4z+qy4iIgLly5eHhYUFrl+/nq8bl9Gr4aeffoKvry/atWuHI0eOFHY4RERUCIx5TuWk4lfUq5IMAJmr2MyZMwcAtE7SfNVpHuh8fX2ZDFCu1Go1Fi1aBACYN29eIUdDRETFgVn2ITh16hSuXLmC6OhorSuwZDVr1ixzXJJKmAEDBmDZsmX4888/cebMGTRt2rSwQyowQUFBsLa2hp+fX2GHQsXAtm3bcP36dfTr18/sK1YREdGrKU9Dhv7++2+MHj1a7/rjL8t4hTbUKspDhl5Fly9fxq5du9C4cWN07969sMMhKpI2b96M0NBQDBs2DBUrVizscIiIqJAY85xqckJw9uxZtGrVSu4R8PHxQdmyZaFU6u90CAoKMuVyRRITAiIiIiIqiox5TjV5yNCcOXOQlpaGGjVqYOvWrahTp46pTRERERERUSExeVLxqVOnIEkSNm3axGSAiIiIiKiYMjkhSExMhJ2dHRo1amTOeIiIiIiIqACZnBBUrFgRarXanLEQEREREVEBMzkh6NOnD5KTk3H8+HFzxkNERERERAXI5FWG4uPj0aBBA9jY2ODo0aNwc3Mzd2xFHlcZIiIiIqKiyOzLjurqBYiMjMS4ceNgaWmJDz/8EE2bNoWjo6Petlq3bp3b5YoNJgREREREVBSZPSFQKBSQJCnPgUmShPT09Dy3U1QwISAiIiKioihf9iHIw4bGZm2DiIiIiIjMx6CEgKsJERERERG9mkxeZYiIiIiIiIo/kxOCBw8e4NGjRwaXj4yMxIMHD0y9HBERERER5QOD5xC8zNvbG2XKlDE4KWjRogUePnz4Sk0qJiIiIiIq7vI0ZMjYScKcVExEREREVLQU2ByC5ORkKJUmd0gQEREREVE+KJCEIDIyEs+ePSuRuxkTERERERVlBn9lf/z4cRw9ejTbsfj4eMyePVtnHSEEYmJisH//fggh0LRpU5MDJSIiIiIi8zM4IQgKCkJAQEC2HYsTEhIQEBCQa10hBGxsbDBt2jTToiQiIiIionxhcELg7e2NNm3ayD8fO3YMlpaWaN68uc46CoUCTk5OqFOnDnx9fVGlSpW8RUtERERERGYlCROX/lEoFPDy8kJkZKS5Yyo2YmNj4ezsDJVKBScnp8IOh4iIiIgIgHHPqSYv+xMYGAhbW1tTqxMRERERURFgckLg6+trzjiIiIiIiKgQFNg+BEREREREVPTkeaewlJQU7NixAydOnEBERAQSEhJ07kgsSRIOHz6c10sSEREREZGZ5CkhOHnyJPr374/IyEgIIeQlSTUJQdYlSrOeJyIiIiKiosHkhODhw4fo3r07VCoV6tatiy5dumDRokVwcHDApEmT8PjxYxw5cgT37t2Du7s7xowZAwsLC3PGTkREREREeWRyQrB06VKoVCp07doVe/fuhSRJckKQdffi77//HhMmTEBwcDB2795tlqCJiIiIiMg8TJ5UfPDgQUiSlGP34peNHTsWAQEB2Lt3L9auXWvq5YiIiIiIKB+YnBA8ePAACoUCDRs2zHY8NTU1R9lx48ZBkiRs3LjR1MsREREREVE+MDkhEELA0dERCsX/N2Fvb4/Y2Ngcqww5OzvD2dkZt27dMj1SIiIiIiIyO5MTgnLlykGlUiElJUU+9tprryEjIwM3b97MVjYxMRExMTFITEw0PVIiIiIiIjI7kxOCqlWrAgDCw8PlY02bNgUArF69OlvZpUuXQggBb29vUy9HRERERET5wOSEoGvXrhBC4I8//pCPjRw5EkIIrFq1Ct26dcP06dPRo0cP+Pn5QZIkDBw40CxBExERERGReUhC17bCuXj48CGGDBmC119/HStWrJCPT5kyBYsXL85sXJLk+QStW7fGwYMHYWVlZYawi4bY2Fg4OztDpVLBycmpsMMhIiIiIgJg3HOqyQmBPocOHcKWLVvw8OFDODs7o0uXLhgyZAiUyjxtjFzkMCEgIiIioqKo0BOCkoIJAREREREVRcY8p5o8h6AgxcTEYMKECWjevDm8vLxgbW2NcuXK4a233sKOHTtyLHOaW1uzZs1C3bp14ejoCHd3dzRu3BgrV65EcnJyPt4FEREREVHRY7Yegri4OFy8eBFPnz4FAJQuXRoNGjQwyzfnoaGhqF+/Ppo1a4YqVarA1dUVT58+xR9//IGnT59i1KhRWLNmTa7txMTEoFGjRrh37x5atmyJpk2bIiUlBX/++Sfu3r2Lt956C3///Xe2vRX0YQ8BERERERVFxjyn5nlQ/+XLlzFz5kwcOHAAarU62zmFQoEuXbpg9uzZaNCggcnX8PHxQUxMTI45CHFxcWjWrBnWrl2LiRMnonbt2nrbWbNmDe7du4fJkydj6dKl8vHU1FS0bNkSR44cwYkTJ9C6dWuTYyUiIiIiKk7yNGQoMDAQTZs2xf79+5GRkQEhRLZXRkYG9u3bh6ZNm2LDhg0mX8fCwkLrhGRHR0d07twZQGYvQm7u3bsHAOjWrVu241ZWVujYsSMAyD0cREREREQlgckJwdmzZzFq1CikpaWhSpUqWLNmDUJDQ5GUlITk5GSEhoZizZo1qFGjBtLT0/Hhhx/i7Nmz5owdycnJOHLkCCRJQq1atXItr+lBOHDgQLbjaWlpOHToEGxtbdG8eXOzxkhEREREVJSZPIegd+/e2L17N9q2bYv9+/fDxsZGa7mUlBR069YNQUFB6NmzJ3bu3GlysDExMVi2bBnUajWePn2K/fv34+HDh/Dz84O/v3+u9ZOSktCmTRucO3cObdq0QePGjZGSkoIDBw7gxYsXWLt2LXr16qWzfkpKClJSUuSfY2NjUb58ec4hICIiIqIipUCWHfXw8EB0dDRu3LiB6tWr6y1769Yt1KpVC25ubnj27JkplwMAhIeHw8fHR/7Z0tISX331FT799FNIkmRQG4mJiRg9ejQ2b94sH1MoFBg/fjxmzpwJd3d3nXX9/f0REBCQ4zgTAiIiIiIqSgokIbCzs4O1tTVevHhhUHkXFxekpqYiISHBlMtlk5GRgYcPH2LLli3w8/ND9+7dsXXr1lw3PouKikLPnj3x9OlTLF++HC1atEBycjL27NmDTz/9FB4eHjh//jxcXFy01mcPAREREREVBwWyylCFChVw//59qNXqXJfpzMjIQHJyMipWrGjq5bKxsLCAt7c3pk6dCgsLC3zxxRdYu3Ytxo4dq7feJ598gpMnTyI4OBh169YFADg7O2PUqFHIyMjA2LFjsWzZMq29AABgbW0Na2trs9wDEREREVFRYPKk4t69eyM1NRW7du3KteyuXbuQkpKCPn36mHo5nTp16gQAOHr0aK5l9+3bB1dXVzkZyOqtt94CAFy4cMGs8RERERERFWUmJwTTp09H1apV8eGHH+p9GD9+/DhGjx6NGjVqYNq0aaZeTqfIyEgAyHW4EJC530BsbCxSU1NznNPMbWAPABERERGVJCYPGfr9998xZswYBAQEoH379mjRogXeeustlCtXDpIkISIiAkFBQThx4gScnZ0xevRo/P7771rbGjJkiN5rXb58GT4+PnB2ds52PDo6Gl9++SUAoGvXrvLxqKgoREVFwd3dPdsk4RYtWuCvv/7CnDlzMGfOHPl4SkqK/HO7du2MeyOIiIiIiIoxkycVKxQKeWUfIYTOVX70nQMASZKQnp6u91qTJk3CunXr0K5dO1SsWBH29va4f/8+9u3bh/j4ePTp0wdbt26V5zJoVgN6eTnSy5cvo3Xr1oiLi0OTJk3kScV//fUX7t27h0aNGuHEiRM6l1B9mTGTNYiIiIiICkqBTSo2dKnPvOrbty9UKhVOnz6N48ePIzExEa6urmjZsiWGDBmCAQMGGBRL/fr1ceHCBcyfPx+HDx/GypUroVQqUaVKFQQEBOCzzz4zOBkgIiIiInoVmNxDQOwhICIiIqKiyZjnVJMnFRMRERERUfHHhICIiIiIqARjQkBEREREVIIxISAiIiIiKsGYEBARERERlWBMCIiIiIiISjAmBEREREREJRgTAiIiIiKiEowJARERERFRCcaEgIiIiIioBFOao5GoqCgEBQXh/v37SExMxKxZs8zRLBERERER5TNJCCFMrZyeno4pU6bgu+++Q2pqqnw8IyND/vOLFy9QuXJlJCYmIiwsDGXKlMlbxEVIbGwsnJ2doVKp4OTkVNjhEBEREREBMO45NU9Dhvr164dly5YhNTUVtWvXhlKZs8PBxcUFgwYNQmpqKnbv3p2XyxERERERkZmZnBD89ttv2L17N0qXLo3z58/jypUrcHV11Vq2X79+AIC9e/eaejkiIiIiIsoHJicEgYGBkCQJixcvRoMGDfSWbdKkCSRJwtWrV029HBERERER5QOTE4KLFy8CAPr06ZNrWVtbWzg7O+PZs2emXo6IiIiIiPKByQmBSqWCs7MzbG1tDSqvVqtNvRQREREREeUTkxMCFxcXqFQqJCcn51o2IiICsbGxKF26tKmXIyIiIiKifGByQlCvXj0AwLFjx3It+8MPPwAAmjZtaurliIiIiIgoH5icEAwcOBBCCMycOROJiYk6y23duhULFy6EJEn44IMPTL0cERERERHlA5N3Kh4yZAhWr16Nc+fOoXnz5hg7dizS0tIAQF6GdMuWLTh8+DCEEGjfvj169OhhtsCJiIiIiCjv8rRT8dOnT9GjRw+cP38ekiRpLSOEQNOmTbFv3z6d+xQUV9ypmIiIiIiKogLbqbh06dL4999/8e2336Ju3bqQJAlCCPlVs2ZNLFu2DMeOHXvlkgEiIiIioldBnnoIXhYfH4/Hjx8jIyMDnp6eKFWqlLmaLpLYQ0BERERERZExz6kmzyHQxsHBAVWqVDFnk0RERERElI/yNGSIiIiIiIiKN5N7CB48eGBUeRsbG5QqVQpWVlamXpKIiIiIiMzM5ITAx8fHpHqVKlVC165dMWHCBA4vIiIiIiIqZCYPGcq6mpAxr7t372LVqlWoV68etm3bZs57ISIiIiIiI5mcEISFhWHr1q1wcXGBu7s7Zs6cicOHD+PWrVu4efMmDh8+jJkzZ8LDwwOurq7Yvn07Ll68iB9++AE1a9ZEUlIShgwZgtDQUHPeDxERERERGcHkZUfv3r2LN954Az4+Pvj777/h5uamtdzz58/RsWNHPHjwABcuXEDFihWRkpKCdu3a4cyZM/joo4/w7bff5ukmCguXHSUiIiKioqhANiabN28eYmNjsXbtWp3JAAC4ublhzZo1iI6Oxrx58wAA1tbWWLBgAYQQOHLkiKkhEBERERFRHpncQ1C+fHnExsZCpVIZVN7JyQkuLi64f/8+ACAjIwP29vawsrJCbGysKSEUOvYQEBEREVFRVCA9BM+ePUNGRobB5dVqNZ4+fSr/bGFhAQcHB6Snp5saAhERERER5ZHJCYGHhweSkpJw/PjxXMseP34ciYmJcHd3l4+lpaXhxYsX2Y4REREREVHBMjkh6NSpE4QQGDlyJMLCwnSWCw8Px8iRIyFJEjp37iwfv3PnDoQQqFixoqkhEBERERFRHpm8MZmfnx+2b9+Ou3fvok6dOujfvz/atGmDsmXLQpIkREZG4ujRo9i6dSsSExPh6OiImTNnyvW3bNkCAGjbtm2eb4KIiIiIiExj8qRiADh16hT69OmDx48fQ5IkrWWEEPD09MT27dvRokUL+fhvv/2Gx48fo0ePHqhcubKpIRQqTiomIiIioqLImOfUPCUEAKBSqfDtt99i+/btuH79ujzR2MLCArVr10bfvn0xfvx4lCpVKi+XKZKYEBARERFRUVSgCUFWaWlpiI6OhhACbm5usLS0NFfTRRITAiIiIiIqiox5TjV5DoE2lpaW8PT0NGeTRERERESUj0xeZYiIiIiIiIo/s/UQPHnyBI8ePUJCQgL0jUJq3bq1uS5JRERERER5lOeEYOXKlVixYgXu3r2ba1lJkrgzMRERERFREZKnhGDAgAHYtm2b3h6BrMw4f5mIiIiIiMzA5DkEW7ZswdatW+Hk5ITt27cjISEBAODl5YX09HREREQgMDAQVapUgbu7Ow4fPgy1Wm3StWJiYjBhwgQ0b94cXl5esLa2Rrly5fDWW29hx44dBicabdu2hSRJel+bNm0yKUYiIiIiouLI5GVHu3Tpgr///hvLly/H+PHjAQAKhQJeXl6IjIyUy8XHx6NNmzYICQnBxYsXUaVKFaOvFRoaivr166NZs2aoUqUKXF1d8fTpU/zxxx94+vQpRo0ahTVr1uTazsaNGxEeHp7jeFpaGubPnw+FQoEHDx6gbNmyBsXFZUeJiIiIqCgqkH0IPD09ERUVhefPn8ubjikUCpQuXRqPHz/OVvbUqVNo0aIFRowYgbVr1xp9rYyMDAghoFRmH+EUFxeHZs2a4caNG7h27Rpq165tyq1gx44d6Nu3L95++23s2bPH4HpMCIiIiIioKDLmOdXkIUMxMTFwcHDItgOxpaWlPHQoq+bNm8POzg6HDh0y6VoWFhY5kgEAcHR0ROfOnQFk9iKYat26dQCAESNGmNwGEREREVFxZHJC4ObmhrS0tGzHSpUqhcTERMTExGit83LPQV4lJyfjyJEjkCQJtWrVMqmNiIgIHDx4EF5eXujevbtZ4yMiIiIiKupMXmWoXLlyePLkCZ49ewYPDw8AQM2aNfHPP/8gKCgIvXv3lstevHgRiYmJcHFxyVOwMTExWLZsGdRqNZ4+fYr9+/fj4cOH8PPzQ9WqVU1qMzAwEGq1GkOHDtXaC5FVSkoKUlJS5J9jY2NNuiYRERERUVFhckLQvHlzXLx4ERcvXpSH7XTv3h3Hjx/HZ599htdeew3169dHcHAwhg0bBkmS0KJFizwFGxMTg4CAAPlnS0tLLF68GJ9++qlJ7QkhEBgYCMCw4ULz58/Pdn0iIiIiouLO5EnFhw4dQqdOnfD+++/LS3XGx8ejZs2aePToESRJkssKIWBpaYnjx4+jadOmeQ46IyMDDx8+xJYtW+Dn54fu3btj69atuX7D/7LDhw+jQ4cOaNOmDY4ePZpreW09BOXLl+ekYiIiIiIqUoyZVGxyD0Hbtm0RFBQEW1tb+ZiDgwOOHDmCoUOH4tSpU/LxChUqYNWqVWZJBoDMScbe3t6YOnUqLCws8MUXX2Dt2rUYO3asUe1oJhOPHDnSoPLW1tawtrY2Ol4iIiIioqLK5B6C3ERERODhw4dwdnY2ecKvIYKDg1G/fn289957+O233wyu9+LFC5QpUwa2traIjIzMltgYisuOEhEREVFRVCA9BA8ePAAAlC5dGjY2NjnOv/baa3jttddMbd5gmk3QjB0utHnzZqSkpGDkyJEmJQNERERERK8Ck5cd9fb2RqVKlRAdHW3OeLS6fPkyVCpVjuPR0dH48ssvAQBdu3aVj0dFReHWrVuIiorS2eb69esBcO8BIiIiIirZTE4IHBwc4OzsjLJly5ozHq02btyIcuXK4e2338b48eMxZcoUDBgwABUrVsTly5fRp08fDBo0SC6/cuVK1KxZEytXrtTa3oULFxAcHIyGDRuiQYMG+R4/EREREVFRZfKQIW9vb4SEhCAjIwMWFhbmjCmHvn37QqVS4fTp0zh+/DgSExPh6uqKli1bYsiQIRgwYEC2VY1yo+kdMHQyMRERERHRq8rkScWzZs3CvHnzsHPnTrzzzjvmjqtY4KRiIiIiIiqKjHlONXnI0JQpU1ClShWMHTsWV65cMbUZIiIiIiIqRCYPGdqxYwdGjx4Nf39/vPHGG+jSpQtatGiB0qVL6x1CNGTIEFMvSUREREREZmbykCGFQiGP2xdCGDSGX5IkpKenm3K5IolDhoiIiIioKCqQfQgqVKhg1EReIiIiIiIqekxOCMLDw80YBhERERERFQaTJxUTEREREVHxx4SAiIjMYsaMGZAkCYsWLSrsUIheWV26dIEkSThy5Ehhh0KvELMkBFFRUdi2bRu+/vprzJ492xxNEhFRMRIREYGlS5fCw8MD48aNy3E+NDQUS5cuxdtvv42KFSvC2toa9vb2qFatGkaMGIELFy7obf/nn3/GhAkT0LJlS3h7e8Pe3h42NjaoUKECevfujR07duQp/lu3bmHDhg0YN24cmjdvDjs7O0iSZNBcuaFDh8plDXndv38/T7G+LDIyEkuXLsX777+PunXrwsvLC1ZWVnByckLdunUxYcIE3Lp1yyzXSklJwapVq/DWW2/By8sL1tbWKFOmDN58801MnToVDx8+1Fs/NjYWCxcuxJtvvgkPDw9YW1vjtddeQ7t27eDv74+YmBizxFkQhBBYv349WrVqBVdXV9jZ2aFatWr45JNP8OTJE531jh49atDnZN26dVrr+/v7AwA+++wzqNXq/Lg1KolEHqSlpYlPPvlE2NjYCIVCIb+yio6OFi4uLsLa2lpERkbm5XJFjkqlEgCESqUq7FCIiArVkCFDBACxePHiHOdOnDghAGR7OTo6CisrK/lnhUIhZs6cqbN9e3v7bPWdnZ2z1QcgOnXqJOLj402Kv02bNjli1LxyM2HCBOHp6an3ZWFhIQCIMmXKiLS0NJNi1GXbtm3Z4lUqlcLFxUVIkiQfs7S0FKtWrcrTda5evSoqV64st2lhYZHjOjt37tRZ/8iRI8LT0zNbnKVKlcoW+6VLl/IUY0FJTk4WnTt3znYvDg4O8s9ubm7i/PnzWusGBQXJ5fR9Zn7++Wed19dc+8cff8yvW6RXgDHPqXlKCHr16iUnAa+//rqwsrLKkRAIIcS4ceOEJEni+++/z8vlihwmBESkS2JqYmGHUGAiIiKEhYWFsLKyEs+fP89xPigoSFhYWIhevXqJbdu2iaioKCGEEOnp6eLs2bOiZcuW8gPSunXrtF5j4sSJ4scffxQhISEiOTlZCCGEWq0Wd+/eFR9//LFcf/To0SbdQ/v27UXNmjXF4MGDxdKlS8Unn3xicEKQm8ePHwulUikAiKlTp+a5vZedPn1azJgxQxw4cEA8efJEqNVqIYQQKSkp4u+//xaNGjUSAIQkSeLkyZMmXePOnTvCzc1NABCNGjUSBw4cECkpKfJ1rl27Jr766itx5swZrfVPnDghbG1tBQDRoUMHceLECZGRkSGEECIxMVGcP39eTJ8+Xdy7d8+k+EwRFhYmAIiKFSsaXXfs2LFyorVy5Ur5vTh37pyoUaOGACC8vLy0Ph9kTQhMtXPnTgFA1K5d2+Q26NVXIAnBli1bhCRJwsvLS1y8eFEIIYSXl5fWhODo0aNCkiTRvXt3Uy9XJDEhICJtjoUfE7ZzbcXx8OOFHUqB8PPzEwBEr169tJ5/+PChuHPnjs76KSkpom7dugKAqFy5skkxDBw4UAAQtra2IjU11ej66enp2X4ODAw0W0KwYMEC+YE8JCQkz+0ZKzo6WtjZ2QkAYuTIkUbXV6vVonnz5gKAePPNN0VionHJbkJCgqhUqZIAIPr06SMnAoXN1ITg9u3bco/P/Pnzc5y/e/eunPzMmDEjx3lzJAQpKSnC1dVVABAnTpwwuR16tRnznGryHILAwEBIkoTFixejQYMGess2adIEkiTh6tWrpl6OiKjYmH5kOpLSkzD9yPTCDiXfif+NowaAQYMGaS3z2muvoWrVqjrbsLKywuDBgwEAd+/exYsXL4yOo1mzZgCApKQkREdHG13fwsLC6DqG2rBhAwCgTZs2qFKlSr5dRxcXFxdUr14dQOZcD2MdPHgQp06dAgCsWbMGtra2RtXftGkT7t27B1tbW6xevfr/2rvvsCiu/m3g99KW3ltEBLvYE3svsTdM7DWxRX2tiT7GnzGixmgSnxhNSGIsYG+JxsSeoogaMRJbiGJFRFGKSu9w3j94drLL7sJSF9j7c117CTPnzJyZAzjfOQ1GRiV79Lh//z7mzJkDHx8fWFtbw9LSEj4+Ppg/fz4ePXpUomOWxK5du5Cbmwtra2vMmTNHbX+dOnUwatQoAPnXXh7MzMwwbNgwAPl1QlRaJQ4Irly5AgDSD2RhLCwsYGdnh7i4uJKejoioSgh6GITzj84DAM49Ooegh0H6LVA5CwsLkx4yu3TpUuLjmJubS1/n5uYWO/+5c+cAANbW1nB1dS1xOcrauXPncOfOHQDA1KlT9VKGuLg43L59GwBQt27dYuffsWMHgPygq0mTJiXO7+vrC2dn52LnB4DNmzfDx8cH/v7+CA8PR05ODoD8weAbNmxAs2bN8Ouvv5bo2MWlOE/Xrl1hZWWlMU3//v0BAJGRkdK9L2tdu3YFAJw8ebJcjk+GpcQBQWJiIuzs7HR+U8CR8ERkCD488yGMZflvm41lxlh2ZpmeS1S+goODAQCenp5wd3cv8XGCgoIAAK+88gqcnJx0ypOYmIjQ0FBMnjwZP/zwAwDgvffe02lmoIqimCnGwcFBpxdoZSU3NxdPnz7FoUOH8PrrryMtLQ2mpqaYPXt2sY91/nx+gNutWzfExsZi/vz5qF27NuRyOVxcXNCnTx/s3bsXQgi1vJmZmQgNDZXyP3jwAFOmTEHNmjUhl8vh7u4OX19fnDhxQuv5Dx8+jHfeeQcAsHjxYjx8+BDp6elITU1FeHg4RowYgaSkJAwfPrxCWgr++ecfAEDTpk21plHep0ivSceOHWFnZwdzc3N4enpi2LBh+OmnnzTey4LatWsHAIiNjS2zWaTIgJW0X5Kbm5swMjIS6enp0jZtYwiioqKETCYr0cCdyoxjCIhI2ZmIMwLLofY5E3FG30UrNxMmTBAAxKBBg0p8jD/++EMYGRkJAIXONCSEEHv37tU4E5BcLheLFy9WGwtQUmUxhiAxMVHquz979uwyKVdRlGe+Uf54enqK06dPF/t4GRkZ0jHmz58vXFxctM5kNGTIEGlwrUJ4eLi0/4MPPpBmizIzM1ObYWjGjBlq58/MzBQeHh4CgNi6davWcg4ZMkQAEPPmzdP52koyhiApKUkq74YNG7SmS0hIkNJ99dVXKvuUxxDgfzNmmZubq2zr37+/SE5OLrI8ipmNAgICdL4GMhwVMoagRYsWAICzZ88Wmfa7774D8G80S0RUHSm3DihU91aC6OhoAICLi0uJ8sfFxWHMmDHIy8tD/fr1sWjRokLTW1hYwM3NDW5ublK/f2NjYyxatAgLFiwo17EAxbVnzx6kpaUBAKZMmVIh53R0dISbmxscHBykbV5eXvjyyy/RrVu3Yh9PeTzHl19+iaSkJHz99ddITEzEixcvEBsbi7lz5wIAfv75Z7z//vta869ZswampqbYu3cvUlJS8PLlSzx69AijR48GAGzcuBEbNmxQyX/ixAk8efIEbm5umDRpktZyTpw4EQBw6tSpYl9jcSQnJ0tfW1paak2nvE85DwDY29tj4cKFuHTpElJTU5GQkIC0tDRcu3ZNGntw4sQJjBkzpsjyKFrTFL+HRCVW0qgjMDBQyGQy0aZNG5GamiqE0NxCsH//fmFqaiqMjIzEkSNHSnq6SoktBESkoK11oLq3EihmB1qwYEGx8yYnJ4v27dtL6xJcu3atWPmzs7PFjRs3pBmG3NzcSjytZkFl0ULQunVraZpOfUhKShIHDx4UDRs2FADEgAEDdHrrrOzJkycqb64/+eQTjekUb+jNzMxETEyMtP3ChQsq+fft26eWNzc3V7z66qsCgHB2dlZZp2HhwoXScQubs9/BwUGaZaogbXmcnZ0FkL8GhrY0c+fO1Xo/Nm/erPW+ZWdnS+lWr15d5H1WpjyN7u+//15oWsV9K07LCBmOCmkhmDhxItq2bYu//voLHTp0wMaNG5GdnQ0ACA0NRUBAAPr06YMxY8YgJycHPXv2xKBBg0p6OiKiSk1T64BCdW4lyMjIAADI5fJi5UtNTcXAgQMREhICa2trHD9+XGp51pWJiQmaNWuGPXv2YM6cOYiJicGoUaOQnp5erOOUhxs3bkh95/U1mNjGxgZvvvkmQkJC4OnpiePHj0ur3BbnGAqmpqaYN2+exnSKlp2srCycOXNGY35PT0/pDbgyIyMjLFiwAAAQHx+vsmq14s13VlYWYmJitH4ULRGa6l5bnvj4eAD5Yxy1pUlMTNR6PxStP5qkpqZqzKOL1atXS4PsDx8+XGhaxThOxe8hUUmVOCAwMjLCzz//jFatWuHvv//GrFmzpF/Idu3aYdq0afj9998hhEC7du2wf//+Mis0EVFlophZKFdonh0nV+RW2xmHFF0WijNVqCIYCA4OhpWVFY4dO4bOnTuXqhzvvvsuACAqKqrQAaoVRTGY2NLSUut0rBXF3t5e6m6zefPmYuW1sbGBra0tAMDb21tlNihlPj4+0teRkZHS1x4eHtLXjRo10noebfkVM07169cPIn/tpCI/BWlLFxERASC/S5W2NNu2bVO7H4oH/CdPnmi9HuV9NWrU0JpOE2tra2lQ8oMHDwpNq5hiV9eB+ETalDggAABXV1dcuHABX331FZo3bw6ZTKbyi+Tj44P169fj7NmzcHR0LKsyExFVKoW1DihU11YCxdgBXef+VwQDZ8+ehaWlJY4dOyZNn1gayg+e9+7dK/XxSiMzMxO7d+8GAIwYMUJ6oNYnxf1JSkpCbGxssfI2a9YMAHSevUk5naOjo3TuwvIrP8grp1PMXFWZ1jFSTL0aFhamNY3yvpJM1aorxe9dScfwECmUKiAA8psQZ82ahatXryIhIQF37tzBrVu38OLFC4SFhWHu3LkwMzMri7ISEVU6RbUOKFTXVoLGjRsDKPpNJpAfDAwYMABnz56FlZUVjh8/XqKBrpoon7+4XTTK2qFDh6QHtYoaTFwU5ftjbW1drLx9+vQBAERERGjtmnLz5k3p69q1a2vMf+vWLa3Tad66dUtj/k6dOgHIf+OumP5U33r37g0gf40Jbd2GFGsDeHl5SYvC6SolJUUKKAreS2XJyclStyflFhaikih1QKDM2toa9erVQ8OGDWFvb1+WhyYiqpR0aR1QqI6tBIq3+9evX0dmZqbWdIpgQNFNqDjBgGIRqsKsWbNG+rqsgoySUqzc3LBhw1It1qarou7Ps2fPEBgYCABo27ZtobPjaDJ+/HiYmpoiOzsb69ev15hm7dq1APK7SL3++usq+xTdlaKiojR2H87Ly8O6desA5LdkvPbaa9K+wYMH45VXXgEAzJs3r9B++4DuLVWlMX78eBgbGyM5ORn+/v5q+x8+fIh9+/YBACZMmKC2X1tQpLB06VIp8PL19dWaLjQ0FHl5eTAxMZECJ6KSKnFAsHr16gpdKpyIqLLRtXVAoTq2EnTq1AkmJibIysrCtWvXNKZJS0vDoEGDEBwcDGtra5w4caJY3YQ++eQTjB8/HidOnEBCQoK0PScnB5cuXcKbb74prYY7duxYqdVCmbe3N2QyGbp3767xHJmZmYiPj5c+KSkp0j7l7fHx8YUutBkREYHTp08DKF7rQFHlK0znzp2xcuVKXL9+XZrcAwASEhKwe/dudOjQAbGxsZDJZFi5cqVa/qCgIMhkMshkMrU+8wBQp04daTDx8uXL8c0330iDd+Pj4zF//nz8/PPPAPIHFytPeQrkr2A9fPhwAMDMmTOxf/9+qZxRUVEYN24crl69CgD4+OOPYWT076OJubk5vvnmG8hkMly5cgWdOnXCqVOnkJWVJaWJiIjAd999h7Zt2+Kbb74p9v0rrgYNGkgLpX344Yf49ttvpfL89ddfGDhwINLT0+Hu7o6FCxeq5W/cuDG++OIL3Lp1S/pZEkLg77//xvjx46WpV/v3749evXppLcelS5cAAK+99lqxW32I1JR0KiOZTCaMjY1Fjx49RGBgoEhKSirpoaosTjtKZNg6B3QWsuWyQqcbLfiRLZeJLgFd9F30MuXr6ysAiCVLlmjcv337dmkaRXNz80Knj3RzcxMXLlxQye/n56cydaW1tbVwdnYWpqamKttHjRqlslimMi8vLwFAdOvWTeN+5WlGi/pERERovRdLly4VAISpqanK9JtFKap8uuQFIIyNjYWjo6Ows7NTu2c7duzQmF95oazAwECNaXJycsTIkSOldCYmJsLR0VFlYbLJkyeL3NxcjflTUlJE165dVRaSU0wVqvgsW7ZM6zXu2rVLWuRNcX4nJychl8tVjrFq1Sqd71tJFiZTyMjIUFkEztTUVNjY2EjfOzk5idDQUI15lctramoqnJychIWFhcr2AQMGFPlc1aFDBwFArF+/vtjlJ8NQnOdUk5IGEp6enoiKikJQUBDOnj2LWbNmYejQoZgwYQL69OmjEuETEVU3qVmpuPT4EgQKb/4vSEAg5HEI0rLTYGlavK4bldX06dPx008/Yc+ePVi1apXa4FHlN+oZGRlFTpGo/PYXACZPngwnJycEBQXh5s2biI2NxcuXL2FtbY1atWqhQ4cOmDBhQqlnKiqtvLw86Q374MGD4erqqlO+7OxsxMXFAQDat29f7PNu374dv/zyC86fP4/IyEjExcUhJycHrq6u8PHxQZ8+fTBp0iSp601JGBsbY//+/RgxYgS2bNmCK1euICEhAa6urmjXrh2mT5+OAQMGaM1vZWWFM2fOICAgADt37kRYWBiSk5Ph4eGBLl26YM6cOejYsaPW/OPGjUPPnj3xzTff4OTJk7h37x4SEhJgbW0NHx8fdO7cGUOHDq2w7mJyuRwnTpzA1q1bsX37doSFhSEjIwP169fHoEGD8P7778PNzU1j3k2bNuHixYv466+/EBMTgxcvXsDU1BR169ZF27ZtMWHCBPTv37/Q80dERODixYuwsLCQFmUjKg2ZEEV0ZitEcHAwduzYgYMHDyIxMVH6T8DNzQ1jx47FhAkTij2vdFWSlJQEOzs7JCYmVopZJIioYsWlxiExM7HohAXYye3gYlV9ZgXJy8tDgwYNcP/+fZw9e7ZMZg0yJOfPn0eXLl1gZ2eHBw8ecFY+KtLKlSvh5+eHSZMmISAgQN/FoUqqOM+ppQoIFDIzM/Hzzz9jx44dOHXqFHJycqTgoGnTppg4cSLGjh1bqrcTlREDAiKifHv37sXYsWPRv39/HD9+XN/FqVIUD3crV67Ehx9+qO/iUCWXmpoKb29vJCcn4/bt2/Dy8tJ3kaiSKs5zapn065HL5RgxYgSOHDmC6OhobNiwAa1bt5YGySxatAi1atUqsgmMiIiqptGjR6Nt27Y4ceKENNiRdHPmzBm4uLhIi6sRFcbf3x/x8fGYO3cugwEqM2XSQqDNnTt3sHPnTuzatQuRkZGQyWTSqoPVAVsIiIj+de3aNRw+fBht2rTBwIED9V0comrJ398fz58/x/z582FnZ6fv4lAlVpzn1BIPKtZFcnIykpKSipw3mIiIqr6WLVuiZcuW+i4GUbU2e/ZsfReBqqEyDwiioqKwa9cu7Ny5E7dv3waQP7+umZkZ3xgREREREVUyZRIQJCcn44cffsDOnTsRHBwMIYS0El+7du0wYcIEjB49mjMnEBERERFVMiUOCPLy8nDq1Cns3LkTP/30EzIyMqQgwMvLC+PHj8fEiRNRv379MissERERERGVrRIHBB4eHoiNjQWQ3yXI1tYWw4cPx8SJEzkHNRERERFRFVHigCAmJgbGxsbo06cPJk6cCF9fX5ibm5dl2YiIiIiIqJyVeB2Czz//HE+ePMGxY8cwatSoQoOBvLw8HDlyBEOHDi3p6YiIiIiIqByUuIVAlwVU7ty5g4CAAOzYsQMxMTElPRUREREREZWTMp92NC0tDQcOHEBAQAAuXLgAANJgYx8fn7I+HRERERERlUKZBQQhISEICAjA/v37kZKSAiA/EGjUqBFGjBiBESNGoGnTpmV1OiIiIiIiKgOlCgji4uKwc+dObN26FeHh4QD+bQ2QyWS4fPkyWrVqVfpSEhERERFRuSh2QCCEwIkTJ7B161YcPXoUOTk5EELAwsICQ4cOxVtvvYV+/foBYBchIiIiIqLKTueA4P79+wgICMD27dvx9OlTCCEgk8nQuXNnTJw4ESNHjoSNjU15lpWIiIiIiMqYzgFB/fr1IZPJIIRAnTp1MGHCBEycOBG1a9cuz/IREREREVE5KnaXoblz5+Kzzz6DmZlZeZSHiIiIiIgqkM4Lk5mZmUEIga+++go1atTArFmzEBISUp5lIyIiIiKicqZzQPDs2TN8+eWXaN68OV68eIFvv/0WnTp1QsOGDbF69Wo8evSo3AqZkJCAuXPnokOHDnB3d4dcLoeHhwd69uyJgwcPSjMb6So5ORl+fn5o2rQpLC0tYW9vj9deew0rVqwopysgIiIiIqqcZKK4T9MArl69ii1btmDv3r1ISEiATCaDTCZD165dMWHCBEyZMgUymQzJycmwtLQsdSHv3buHli1bon379qhXrx4cHR0RGxuLI0eOIDY2FtOmTcOmTZt0OtajR4/Qs2dPPHjwAL169cKrr76KzMxM3Lt3D48ePcKNGzd0LldSUhLs7OyQmJgIW1vbkl4eEREREVGZKs5zaokCAoXMzEz88MMP2Lp1K86ePSvNPKT49+DBgxg0aBBMTEq3/llubi6EEGrHSU5ORvv27XHz5k2EhYWhSZMmRR6nQ4cOCAsLw7Fjx9CjRw+V/Tk5OcUqKwMCIiIiIqqMivOcqnOXIU3kcjnGjRuH06dP4969e1iyZAk8PDwA5K9XMGzYMLi6umLSpEk4fvw4cnJySnQeY2NjjQ/qNjY26Nu3L4D8VoSi/PDDD7h8+TIWLlyoFgwAKHXgQkRERERU1ZSqhUATIQROnTqFLVu24MiRI8jOzoZMJgMA2Nvb4/nz52V2royMDLRv3x43btzA7du3Ub9+/ULTv/nmm/jxxx9x48YN2Nvb49ixY0hISEDdunXRv39/WFtbF+v8bCEgIiIiosqoOM+pZf5KXCaToV+/fujXrx/i4+OxY8cOBAYG4p9//kFCQkKpjp2QkID169cjLy8PsbGxOH78OKKiouDn51dkMAAAoaGhAIDz58/j3XffRWZmprTPxcUFBw4cQPfu3bXmz8zMVMmTlJRU8oshIiIiIqoEyryFQJuQkBAEBAToPPhXk4cPH6oshGZqaorVq1djwYIFUitEYczNzZGZmQljY2MsXLgQs2fPhrm5Ofbu3YuFCxfCwsICt27dwiuvvKIx//LlyzXORMQWAiIiIiKqTCpsULG+5ObmIioqCvv27YOfnx8GDhyIAwcOFDkGwMzMDNnZ2fD19cXhw4dV9i1evBiffvopPvroIyxdulRjfk0tBJ6engwIiIiIiKhSqbBBxfpibGwMb29vLF68GKtWrcKPP/6IzZs3F5nPzs4OADBkyBC1fYMHDwbwb7ciTeRyOWxtbVU+RERERERVWZUMCJT16dMHABAUFFRk2oYNGwLIH9xckGJbenp6WRWNiIiIiKjSq/IBQXR0NADdpgzt2bMnAODmzZtq+xTbvL29y65wRERERESVXJUICK5du4bExES17S9evMCSJUsAAP3795e2x8fHIzw8HPHx8SrpJ02aBLlcjq+++gpPnjyRticnJ2P16tUAgJEjR5bHJRARERERVUpVIiDYtm0bPDw8MHjwYMyePRvvv/8+Ro8eDS8vL1y7dg3Dhg3D2LFjpfT+/v7w8fGBv7+/ynFq166NtWvXIjY2Fi1atMC0adMwe/ZsNG/eHNeuXcM777yD119/vaIvj4iIiIhIb6rE0rzDhw9HYmIiQkJCEBwcjLS0NDg6OqJz586YOHEiRo8erdO0owAwZ84ceHt7Y+3atdi3bx9ycnLQpEkTLFmyBNOmTSvnKyEiIiIiqlyq5LSjlQVXKiYiIiKiyqjaTztKRERERERlgwEBEREREZEBY0BARERERGTAGBAQERERERkwBgRERERERAaMAQERERERkQFjQEBEREREZMAYEBARERERGTAGBEREREREBowBARERERGRAWNAQERERERkwBgQEBEREREZMAYEREREREQGjAEBEREREZEBY0BARERERGTAGBAQERERERkwBgRERERERAaMAQERERERkQFjQEBEREREZMAYEBARERERGTAGBEREREREBowBARERERGRAWNAQERERERkwBgQEBEREREZMAYEREREREQGjAEBEREREZEBY0BARERERGTAGBAQERERERkwBgRERERERAaMAQERERERkQFjQEBEREREZMAYEBARERERGTAGBEREREREBowBARERERGRAWNAQERERERkwBgQEBEREREZMAYEREREREQGjAEBEREREZEBY0BARERERGTAGBAQERERERkwBgRERERERAaMAQERERERkQFjQEBEREREZMAYEBARERERGTAGBEREREREBqxKBAQJCQmYO3cuOnToAHd3d8jlcnh4eKBnz544ePAghBA6HScoKAgymUzrJyQkpJyvhIiIiIiocjHRdwF0ER8fj4CAALRv3x5Dhw6Fo6MjYmNjceTIEQwfPhzTpk3Dpk2bdD5et27d0L17d7XtNWvWLMNSExERERFVflUiIKhduzYSEhJgYqJa3OTkZLRv3x6bN2/GvHnz0KRJE52O1717dyxfvrwcSkpEREREVLVUiS5DxsbGasEAANjY2KBv374AgHv37lV0sYiIiIiIqrwq0UKgTUZGBk6fPg2ZTIbGjRvrnO/u3bv48ssvkZaWBi8vL/Tu3RvOzs7lWFIiIiIiosqpSgUECQkJWL9+PfLy8hAbG4vjx48jKioKfn5+qF+/vs7H2bNnD/bs2SN9b2FhgRUrVuA///lPofkyMzORmZkpfZ+UlFT8iyAiIiIiqkSqXECwYsUK6XtTU1OsXbsWCxYs0Cm/i4sL1q5di0GDBqFWrVpISEjAmTNn8P7772PRokWwtbXF9OnTteZfs2aNyvmJiIiIiKo6mdB1zs5KJDc3F1FRUdi3bx/8/PwwcOBAHDhwQOM4A12EhYWhVatWcHBwQHR0NIyMNA+t0NRC4OnpicTERNja2pbo3EREREREZS0pKQl2dnY6PadWiUHFBRkbG8Pb2xuLFy/GqlWr8OOPP2Lz5s0lPl7Tpk3Rrl07xMTEFDo4WS6Xw9bWVuVDRERERFSVVcmAQFmfPn0A5C86VhqKQcVpaWmlLRIRERERUZVR5QOC6OhoAChxdyEAyMnJwZUrVyCTyVCrVq2yKhoRERERUaVXJQKCa9euITExUW37ixcvsGTJEgBA//79pe3x8fEIDw9HfHy8SvqLFy+i4JCJnJwc/Oc//0FkZCT69u0LR0fHcrgCIiIiIqLKqUrMMrRt2zZs2bIFPXr0gJeXF6ysrBAZGYljx44hJSUFw4YNw9ixY6X0/v7+WLFiBfz8/FRWJB4zZgxkMhk6duwIDw8PJCQkIDg4GLdv30atWrWwceNGPVwdEREREZH+VImAYPjw4UhMTERISAiCg4ORlpYGR0dHdO7cGRMnTsTo0aMhk8mKPM7MmTNx8uRJBAUFIT4+HiYmJqhXrx4++OADLFiwAA4ODhVwNURERERElUeVnHa0sijOdE5ERERERBWl2k87SkREREREZYMBARERERGRAWNAQERERERkwBgQEBEREREZMAYEREREREQGjAEBEREREZEBY0BARNXS0qVLIZPJ8Nlnn+m7KETVVr9+/SCTyXD69Gl9F4WISoEBARFVO48fP8a6devg4uKCWbNmqe1/9uwZDhw4gMWLF6N3795wcnKCTCaDTCZDUFBQkcffvXs35s6di86dO8Pb2xtWVlYwNzdHrVq18MYbb+DgwYOlvobk5GSsWbMGbdu2hZ2dHSwsLFCvXj3MmjULERERReZ//PgxFi1ahJYtW8LGxgampqZwcXFBjx494O/vj8zMzFKXUZPo6GisW7cO48aNQ/PmzeHu7g4zMzPY2tqiefPmmDt3LsLDw8v8vPv27ZPqUCaT4eHDh0XmSUpKwqeffoqOHTvCxcUFcrkcNWvWRI8ePbB8+XIkJCSUeTnLixACW7duRZcuXeDo6AhLS0s0aNAA7733HmJiYrTmCwoKUrlv2j5btmzRmH/58uUAgIULFyIvL688Lo2IKoKgEktMTBQARGJior6LQkRKJk6cKACItWvXatzv5+cnAGj8nDlzpsjjW1lZqeSxs7MTZmZmKtv69OkjUlJSSlT+27dvizp16kjHksvlwt7eXvreyspKHDt2TGv+U6dOCWtraym9kZGRsLOzUylfkyZNRHR0dInKV5jvv/9e5TwmJibCwcFByGQyaZupqan4+uuvy+yccXFxwsXFReW8ERERheY5ffq0cHNzUymn8j0GIK5evVpmZSxPGRkZom/fvirXolz/Tk5OIjQ0VGPeM2fOSOnc3Ny0fnbv3q31/Ipzb9++vbwukYhKoDjPqQwISoEBAVU66bFCJN0t/ic9Vt8lLzOPHz8WxsbGwszMTDx//lxjmuXLlwtPT0/h6+srVqxYITZv3lysgGDevHli+/bt4u7duyIjI0MIIUReXp64f/++mDNnjnSs6dOnF7v8aWlpol69egKAcHR0FAcPHhTZ2dlCCCHu378vBg8eLAAIS0tLcefOHbX8z58/lx5sa9WqJY4ePSqysrKEEEIkJSWJ9evXCxMTEwFADBkypNjlK0pISIhYunSpOHnypIiJiRF5eXlCCCEyMzPFr7/+Klq1aiUACJlMJv74448yOefo0aMFANGxY0edAoLz588LCwsLAUD06tVLnD9/XuTm5goh8u9/aGio+OCDD8SDBw/KpHy6iIiIEACEl5dXsfPOnDlTCrT8/f1FZmamEEKIy5cvi0aNGgkAwt3dXeP/VcoBQUn9+OOPUpBJRJUHA4IKwoCAKpXsFCH2mAqxG8X/7DEVIjtV31dQJhRv/4cOHao1TU5Ojsr3iocxXQOCoowZM0YAEBYWFtLDuK78/f2lshw+fFhtf1ZWlqhfv74AIIYPH662f9u2bUVey9KlS6WWg9TUiq33Fy9eCEtLSwFATJ06tdTH++mnnwQA0blzZxEQEFBkQJCamiq1vgwbNkwKBPStpAHB7du3hbGxsQAg1qxZo7b//v37UvCzdOlStf1lERBkZmYKR0dHAUCcP3++xMchorJVnOdUjiEgqi5MrADndgBkxcwoA5zbAyaW5VGqCiX+148aAMaOHas1nbGxcbmWo3379gCA9PR0vHjxolh5jx49CgCoX78+fH191fabmppi/vz5AICffvpJ7fhPnz6Vvm7durXGc7Rt2xYAkJeXh/T09GKVr7QcHBzQsGFDAPnjHEojISEBM2fOhJmZGTZv3gyZrOif/Z07d+LBgwewsLDAxo0bYWRUsv8G79+/jzlz5sDHxwfW1tawtLSEj48P5s+fj0ePHpXomCWxa9cu5ObmwtraGnPmzFHbX6dOHYwaNQpA/rWXBzMzMwwbNgwAsGnTpnI5BxGVLwYERNVJ84+Q/7KvOATQfGV5lKbChYWFSQ+ZXbp00Vs5zp07BwCwtraGq6trsfJGRkYCABo3bqw1jY+PDwAgOztbbXaXOnXqSF+HhoZqzP/nn38CALy9veHk5FSs8pVWXFwcbt++DQCoW7duqY713nvvITo6Gh988AEaNWqkU54dO3YAAHx9feHs7Fyi827evBk+Pj7w9/dHeHg4cnJyAADh4eHYsGEDmjVrhl9//bVExy4uxXm6du0KKysrjWn69+8PIP9nS3Hvy1rXrl0BACdPniyX4xNR+WJAQFSduHUHXDoDMh3fgMuMAZcu+fmqgeDgYACAp6cn3N3dK/TciYmJCA0NxeTJk/HDDz8AyH9g1eWttSa5ubk67fv7779V9g0aNAg1a9YEALz99ts4fvw4srOzAeTPXLRhwwZ8+umnMDExwfr160tUtuLKzc3F06dPcejQIbz++utIS0uDqakpZs+eXeJj/vrrrwgMDETjxo2xePFinfJkZmZKQVK3bt3w4MEDTJkyBTVr1oRcLoe7uzt8fX1x4sQJrcc4fPgw3nnnHQDA4sWL8fDhQ6SnpyM1NRXh4eEYMWIEkpKSMHz48AppKfjnn38AAE2bNtWaRnmfIr0mHTt2hJ2dHczNzeHp6Ylhw4bhp59+ghBFv2Ro164dACA2NrZcZpEiovLFgICoumn+ESC0P0yqELnVpnUAAC5dugQAaNGiRYWcT3mqS3t7e7Rp0waBgYGQy+VYvHgxli1bVuxjent7A8h/cNP2IBYWFiZ9HR0drbLP0tISx48fR926dREZGYmBAwfC3Nwc9vb2sLW1xXvvvYdu3brh999/19glqSwp5qg3MTFBjRo1MGzYMPz999/w9PTEqVOndH6rX1BKSgqmTZsGmUyGTZs2wczMTKd8Dx8+RFZWFoD87krNmzdHQEAA4uLiYGlpiZiYGPz8888YMGAAZs6cqZY/KytLCmI2btyINWvWwMvLS/oZaNiwIQ4cOIAhQ4YgKSkJ69atK9H16So5ORnJyckAAA8PD63plPcV/HlRdvHiRelaHj9+jEOHDmHo0KEYOHAgUlJSCi1L/fr1YW1tLR2HiKoWBgRE1Y2urQTVrHUA+Pdhx8XFpULOZ2FhATc3N7i5uUnjEoyNjbFo0SIsWLCgRGMVBg4cCACIiIjAnj171Panp6fjiy++kL5PSkpSS9OsWTOcPXsWvXv3BpA/ViAxMVH6OiUlBXFxccUuW3E5OjrCzc0NDg4O0jYvLy98+eWX6NatW4mP+/777yMyMhIzZsxAp06ddM738uVL6es1a9bA1NQUe/fuRUpKCl6+fIlHjx5h9OjRAPIf+Dds2KCS/8SJE3jy5Anc3NwwadIkreeZOHEiAODUqVPFuaxiUwQDQH4gqI3yPuU8AGBvb4+FCxfi0qVLSE1NRUJCAtLS0nDt2jVp7MGJEycwZsyYIsuj6H5WWNBBRJUTAwKi6kiXVoJq1joAQHrIdXR0rJDz+fr64tmzZ3j27BkyMjJw48YNjBw5Eh999BGaNm1aojelkyZNksYBTJ8+HRs3bkR8fDyysrIQEhKCvn374tGjRzA1NQUAjYNid+3ahXr16uHPP//E+vXrcf/+faSmpuLvv//GrFmz8Oeff2L48OFYvXp16W5AEfbs2YNnz57hxYsXSEpKwsGDB2Fubo433ngDgwcPLvKtsybnzp3Dt99+ixo1auCTTz4pVl7lhbPy8vKwceNGjB49WrqXnp6e2L17N1599VUAwKpVq6TxAQBw/vx5APmBxSuvvAJ3d3eNn2nTpgH4dzyIMm152rRpAwCIiorSmmbevHnFul6FwrqttWzZEmvXrkXbtm2lwEEmk6FFixbYt2+fNFD56NGjRa5GrPi9q4hgk4jKFgMCouqoqFaCatg6AAAZGRkAALlcXuHnNjExQbNmzbBnzx7MmTMHMTExGDVqVLFn8bG0tMSxY8dQu3ZtpKamYubMmdIquh06dMC5c+cwe/Zs1K9fHwBU3r4D+QOGJ06ciMzMTBw6dAjz5s1DnTp1YGlpiaZNm8Lf3x8ffPABAMDPz6/C+nvb2NjgzTffREhICDw9PXH8+HFplVtdpaenY8qUKRBC4Ouvv4atrW2xy6Dg6ekpvQFXZmRkhAULFgAA4uPj8ddff0n7FG++s7KyEBMTo/WjaInQVPfa8sTHxwPID1S0pVG08mi6nrS0NK3XnZqaqjGPLlavXg1zc3MA+eMnCmNhYQHg399DIqo6GBAQVVeFtRJUw9YB4N8uC8pdQ/Th3XffBZD/trewAaraNGrUCDdu3MC6devw+uuvo3bt2qhXrx58fX1x9OhRrF+/Hg8fPgQANGjQQCXv559/DiEEXn31VfTs2VPj8RcuXAgAyMnJwc8//1zs8pWGvb291N1m8+bNxcr72Wef4e7duxgwYAB69eqFlJQUlU9mZqaUNi0tDSkpKSoPp8p96Qsbv6CYxQlQfcuvGMzdr18/iPx1fIr8FKQtXUREBID8LlXa0mzbtk3lWDY2NtID/pMnT7Rej/K+GjVqaE2nibW1tTQo+cGDB4WmVUyBW9EzVxFR6ZnouwBEVE4UrQTxF1UDA5kx4Nyx2rUOAP+OHSju3P9lTfnB8969eyU6hrW1Nd59910puFB26dIl6Y1wwT70N2/eBFD4lJ62trZwc3NDTEyM9CBakRT3JykpCbGxsTpPzaoo6/Hjx4t8092kSRMA+d26FG+2HR0d4eHhgSdPnhTajUb5QV45nWLmqoIzO+lTkyZNEBISojLQvCDlfYr7Uh4Uv3cVNYaHiMoOWwiIqjNNrQTVtHUA+Hfu/qLeZJY35fMXt4uGLrZs2QIg/022ou+5gmJMgab+6wqZmZlISEgot/IVRfn+KGamqSh9+vQBANy6dUvrLE63bt2Svq5du7b0tSL4evLkiTSeQN8UA8fPnTuntduQYm0ALy8vaVE4XaWkpEgBhfK9KCg5OVnq9qTcwkJEVQMDAqLqrOBYgmo6dkBBsTjS9evXVbqPlCXlQabarFmzRvq6NLPpaHLmzBkEBAQAgMZpTV977TUAwF9//YXLly9rPEZgYKB0fzp06FCm5Svq/jx79gyBgYEAoDKQVRfbtm0rtHuO4rhAfmuCEEKt37uiu1JUVBT279+vdo68vDxpulAPDw/pfgLA4MGD8corrwAA5s2bV2i/faBiWqrGjx8PY2NjJCcnw9/fX23/w4cPsW/fPgDAhAkT1PYXtcbA0qVLpW5XhU1TGxoairy8PJiYmBRr5iciqiQElVhiYqIAIBITE/VdFCLtnp0RYjf+/Tw7o+8SlZvExERhYmIiAIiQkBCt6XJzc0VcXJz0uXLlikD+Es/i8OHDKvsyMjJU8n700Udi3Lhx4vjx4+Lly5fS9uzsbBESEiLeeOMN6Vhjx47VeH4vLy8BQHTr1k3j/vXr14vdu3eLZ8+eSduePn0qPvnkE2FpaVnosS9fvixkMpkAIGrUqCH27dsnUlNThRBCxMXFiZUrVwozMzMBQDRo0EBkZWUVu3yFadeunVixYoW4du2ayrFfvnwpdu3aJby9vQUAIZPJxMmTJ9XynzlzRrp/gYGBxTp3YGCglDciIkJruuHDhwsAwt7eXuzbt08q56NHj8To0aOlY2zbtk0t748//ijd35YtW4qTJ0+KzMxMaf+DBw/Exo0bRZs2bcRHH32kc9kjIiIEAOHl5aVzHoWZM2cKAMLMzEx88803UnlCQ0NF48aNBQDh7u4uEhIS1PI2atRIrFu3Tty8eVPk5uYKIYTIy8sTN27cEOPGjZPuRf/+/Qstw5o1awQA0bZt22KXn4jKR3GeUxkQlAIDAqoyfumcHwz80kXfJSl3vr6+AoBYsmSJ1jSKhy9dPgUfSv38/FT2W1tbC2dnZ2FqaqqyfdSoUSI9PV3j+Yt64FZcAwBhbm4ubG1tVY49ZcoUkZ2drfX6vvvuO5XyyGQyYWNjo3KMWrVqiVu3bpWofIVR5AUgjI2NhaOjo7Czs1O7Zzt27NCYvyICgpSUFNG1a1cprVwuFw4ODiplXLZsmdb8u3btkgIzAMLExEQ4OTkJuVyucoxVq1bpXPbSBAQZGRmib9++0nlNTU1V6tvJyUmEhoZqzKtcXlNTU+Hk5CQsLCxUtg8YMEAkJSUVWoYOHToIAGL9+vXFLj8RlY/iPKdyUDGRIWjxMXCmf/6/1dz06dPx008/Yc+ePVi1alWhg0dLYvLkyXByckJQUBBu3ryJ2NhYvHz5EtbW1qhVqxY6dOiACRMmoHPnziU+x/Tp02FnZ4c///wT0dHRyMrKQu3atdGlSxdMmzatyGO/88476NKlC7755hsEBQXh4cOHSEtLg4ODAxo3bowhQ4ZgxowZGqftzM7OluaRb9++fbHLvn37dvzyyy84f/48IiMjERcXh5ycHLi6usLHxwd9+vTBpEmTpK43+mBlZSV1vdq5cyfCwsKQnJwMDw8PdOnSBXPmzEHHjh215h83bhx69uyJb775BidPnsS9e/eQkJAAa2tr+Pj4oHPnzhg6dGiZdxfTRi6X48SJE9i6dSu2b9+OsLAwZGRkoH79+hg0aBDef/99uLm5acy7adMmXLx4EX/99RdiYmLw4sULmJqaom7dumjbti0mTJiA/v37F3r+iIgIXLx4ERYWFtKibERUtciEKKIDIWmVlJQEOzs7JCYmFns+bKIKl5MOmFjouxTlLi8vDw0aNMD9+/dx9uxZaVwB6eb8+fPo0qUL7Ozs8ODBgwpb5I2qrpUrV8LPzw+TJk2SxrcQkf4V5zmVg4qJDIUBBANA/iw7H330EQAUeyVbgrQa7YIFCxgMUJFSU1Px1VdfQS6Xw8/PT9/FIaISYkBARNXO6NGj0bZtW5w4cQKXLl3Sd3GqlDNnzsDFxUXj+gdEBfn7+yM+Ph5z586Fl5eXvotDRCXEMQREVO3IZDJ89913OHz4sDQ3OunmzJkz+i4CVSFWVlZYvnw55s+fr++iEFEpcAxBKXAMARERERFVRhxDQEREREREOmFAQERERERkwBgQEBEREREZMAYEREREREQGjAEBEREREZEBY0BARERERGTAGBAQERERERkwLkxWCoolHJKSkvRcEiIiIiKifymeT3VZcowBQSkkJycDADw9PfVcEiIiIiIidcnJybCzsys0DVcqLoW8vDxER0fDxsYGMpmsws6blJQET09PREVFcYXkSoJ1UrmwPiof1knlwvqofFgnlUt1qA8hBJKTk1GjRg0YGRU+SoAtBKVgZGSEmjVr6u38tra2VfaHtLpinVQurI/Kh3VSubA+Kh/WSeVS1eujqJYBBQ4qJiIiIiIyYAwIiIiIiIgMGAOCKkgul8PPzw9yuVzfRaH/YZ1ULqyPyod1UrmwPiof1knlYmj1wUHFREREREQGjC0EREREREQGjAEBEREREZEBY0BARERERGTAGBAQERERERkwBgRVyOXLlzFgwAA4ODjAysoKbdu2xZ49e/RdrGpv165dmD59Olq3bg25XA6ZTIZt27ZpTZ+UlIT33nsPXl5ekMvl8PLywnvvvYekpKSKK3Q19eTJE6xfvx59+vRBrVq1YGZmBnd3dwwbNgyXLl3SmIf1Ub4SEhIwd+5cdOjQAe7u7pDL5fDw8EDPnj1x8OBBaJq3gnVSsT777DPIZDLIZDKEhIRoTMM6KV/e3t5SHRT8zJgxQy0966Ni/Pjjj+jduzecnJxgYWGB2rVrY8yYMYiKilJJZwj1wVmGqoigoCD07dsXZmZmGD16NOzs7HDo0CFERETg448/xpIlS/RdxGrL29sbkZGRcHZ2hpWVFSIjIxEYGIi3335bLW1qaio6d+6Ma9euoXfv3njttddw/fp1nDx5Ei1btsT58+dhZWVV8RdRTSxevBiffvop6tati27dusHV1RV3797F4cOHIYTA3r17MXLkSCk966P83bt3Dy1btkT79u1Rr149ODo6IjY2FkeOHEFsbCymTZuGTZs2SelZJxXr1q1bePXVV2FiYoLU1FRcvHgR7du3V0nDOil/3t7eSEhIwPz589X2tW7dGoMGDZK+Z32UPyEEZsyYgU2bNqFu3bro27cvbGxsEB0djbNnz2L37t3o3LkzAAOqD0GVXnZ2tqhbt66Qy+XiypUr0vakpCTRpEkTYWJiIu7cuaPHElZvv/76q3j48KEQQog1a9YIACIwMFBj2mXLlgkAYtGiRRq3L1u2rLyLW60dPHhQBAcHq20PDg4WpqamwtHRUWRkZEjbWR/lLycnR2RnZ6ttT0pKEo0bNxYARFhYmLSddVJxcnJyRJs2bUTbtm3F+PHjBQBx8eJFtXSsk/Ln5eUlvLy8dErL+ih/GzZsEADErFmzRE5Ojtp+5b9phlIfDAiqgFOnTgkAYtKkSWr79u3bJwCI//u//9NDyQxPYQFBXl6eqFGjhrC2thYpKSkq+9LT04WDg4Pw8PAQeXl5FVRaw9KnTx8BQFy+fFkIwfqoDN59910BQBw+fFgIwTqpaB9//LEwMzMTYWFh4q233tIYELBOKoauAQHro/ylpaUJR0dHUadOHY0vM5QZUn1wDEEVEBQUBADo06eP2j7FtrNnz1ZkkUiDu3fvIjo6Gp06dVJrPjQ3N0fXrl3x5MkT3Lt3T08lrN5MTU0BACYmJgBYH/qWkZGB06dPQyaToXHjxgBYJxUpLCwMK1aswNKlS9GkSROt6VgnFSczMxPbt2/H6tWr8e233+L69etqaVgf5e/XX3/FixcvMHToUOTm5uLQoUP45JNPsHHjRrX7akj1YaLvAlDR7t69CwCoX7++2j4HBwc4OztLaUh/Cqsn5e13797VmoZK5tGjR/jtt9/g7u6OZs2aAWB9VLSEhASsX78eeXl5iI2NxfHjxxEVFQU/Pz+Vew2wTspbTk4O3n77bfj4+GDx4sWFpmWdVJxnz56pjT3r168fdu7cCWdnZwCsj4oQGhoKIP/lUYsWLXD79m1pn5GREd59913897//BWBY9cGAoApITEwEANjZ2Wncb2tri8ePH1dkkUgDXepJOR2VjezsbEyYMAGZmZn47LPPYGxsDID1UdESEhKwYsUK6XtTU1OsXbsWCxYskLaxTirG6tWrcf36dVy6dElqOdOGdVIxJk+ejG7duqFJkyaQy+W4efMmVqxYgRMnTmDIkCG4cOECZDIZ66MCxMbGAgA+//xzvPbaa/jzzz/h4+ODq1ev4p133sHnn3+OunXrYubMmQZVH+wyRERVVl5eHiZPnozg4GBMmzYNEyZM0HeRDJa3tzeEEMjJyUFERARWrlyJDz74AMOGDUNOTo6+i2cwrl+/jlWrVmHhwoV47bXX9F0c+p9ly5ahW7ducHZ2ho2NDdq1a4ejR4+ic+fOuHjxIo4fP67vIhqMvLw8AICZmRkOHz6MNm3awNraGl26dMEPP/wAIyMjfP7553ouZcVjQFAFKCJTbRFoUlKS1uiVKo4u9aScjkpHCIFp06Zh165dGD9+PDZu3Kiyn/WhH8bGxvD29sbixYuxatUq/Pjjj9i8eTMA1klFeOutt1C3bl0sX75cp/SsE/0xMjLCpEmTAAAXLlwAwPqoCIp717p1a9SoUUNlX5MmTVCnTh3cv38fCQkJBlUfDAiqgIL9b5W9fPkS8fHxVb7vWnVQWD0pb2ddlV5eXh6mTJmCgIAAjBkzBtu2bYORkeqfM9aH/ikmPVBMjMA6KX/Xr19HeHg4zM3NVRa/2r59OwCgQ4cOkMlkOHz4MADWib4pxg6kpaUBYH1UhIYNGwIA7O3tNe5XbE9PTzeo+uAYgiqgW7duWLNmDX755ReMHj1aZd8vv/wipSH9ql+/PmrUqIELFy4gNTVVZUaCjIwMBAcHo0aNGqhXr54eS1n15eXlYerUqQgMDMSoUaOwc+dOadyAMtaH/kVHRwP4d+Yn1kn5mzJlisbtwcHBuHv3LoYMGQIXFxd4e3sDYJ3om2KFddZHxenRoweA/EX7CsrOzsa9e/dgZWUFFxcXuLu7G0596HveUypadna2qFOnjpDL5eLq1avSduWFyW7fvq2/AhoQLkymX7m5ueLtt98WAMSIESOKnEOa9VH+rl69KhISEtS2P3/+XLRs2VIAEDt37pS2s070Q9s6BEKwTsrbP//8I16+fKm2/dy5c8Lc3FzI5XIRGRkpbWd9lD/FujWbN29W2b5y5UoBQIwfP17aZij1IRNCiIoOQqj4zpw5g759+0Iul2PMmDGwtbXFoUOHEBERgVWrVuGDDz7QdxGrrS1btuD8+fMAgL///htXrlxBp06dpDcCQ4cOxdChQwGoL3HeqlUrXL9+HSdOnKheS5zryfLly7FixQpYW1tj3rx50ptnZUOHDkXLli0BsD4qwvz587Flyxb06NEDXl5esLKyQmRkJI4dO4aUlBQMGzYMBw4ckLp0sU704+2338b27dtx8eJFtG/fXmUf66R8LV++HJ999hlef/11eHt7Qy6XIywsDL/88guMjIywceNGTJ06VUrP+ih/9+/fR8eOHREbG4uBAweiUaNGuHr1Kk6fPg0vLy+EhITA3d0dgAHVh74jEtLdpUuXRL9+/YSdnZ2wsLAQrVu3Frt27dJ3sao9xZs1bR8/Pz+V9AkJCeLdd98Vnp6ewtTUVHh6eop3331X41tUKp6i6gIaWm9YH+Xr3Llz4u233xaNGjUStra2wsTERLi6uop+/fqJPXv2aFzBk3VS8QprIRCCdVKegoKCxMiRI0W9evWEjY2NMDU1FTVr1hSjR48Wly5d0piH9VH+Hj16JN5++23h7u4u3eNZs2aJmJgYtbSGUB9sISAiIiIiMmCcZYiIiIiIyIAxICAiIiIiMmAMCIiIiIiIDBgDAiIiIiIiA8aAgIiIiIjIgDEgICIiIiIyYAwIiIiIiIgMGAMCIiIiIiIDxoCAiIiIiMiAMSAgIiI13bt3h0wmw/Lly/VdlHIRFBQEmUwGmUym76KUq99//x0ymQwDBgzQd1GqrYcPH0o/Sw8fPiz18UJCQiCTydC1a9fSF45IRwwIiCqBly9fwtzcXPpP5e7du/ouElGVlJCQgOXLl2P58uVISEjQd3H0Ki8vDwsWLACAahvYVUft27dH3759ce7cOfz444/6Lg4ZCAYERJXA7t27kZmZKX0fEBCgx9IQAbVq1ULDhg3h7Oys76IUS0JCAlasWIEVK1YUGhBYWlqiYcOGaNiwYcUVroJt374d169fx8CBA9G2bVt9F4eKQRHALV68GDk5OfotDBkEBgRElcDWrVsBAHPmzAGQ/x95bm6uPotEBm7Hjh0IDw/H7Nmz9V2UctG2bVuEh4cjPDxc30UpN5999hkAYObMmXouCRVX+/bt0aJFC9y5cweHDx/Wd3HIADAgINKzK1eu4Nq1a7C3t8dnn32GOnXq4OnTpzhx4oS+i0ZEVVRQUBDCw8Ph4uKCvn376rs4VALjx48HAHz33Xd6LgkZAgYERHqmaB0YNWoUzM3NMWHCBJXtytatWweZTAY3N7dCm5GFEPDy8oJMJsOqVavU9ufm5mLbtm3o27cv3NzcYGZmJj047Nu3D0IIjcf19vaGTCbDtm3bkJKSgmXLlqFZs2awsbFRGVCXnZ2NX3/9FXPnzkXr1q3xyiuvwMzMDK6urujbty/27t2r9RwKkZGRmDJlCmrWrAm5XI6aNWti0qRJuHfvnk6D+Ep6jUXR9R68/fbbkMlkePvtt7Uea9u2bZDJZPD29lbbVzD/Dz/8gO7du8PR0RGWlpZo2bIlNmzYgLy8PI3HVh4ULITA5s2b0a5dO9ja2sLGxgYdOnTArl27tJatsEHFyvcgKysLa9euRYsWLWBlZQU7Ozv07NkTJ0+e1HpsAEhNTYWfnx98fHxgYWEBV1dXDBgwAL///rvaOXTVvXt31K5dW/q+du3a0s+JTCZD9+7dpX2FDSouWC/nzp3D4MGD4erqCisrK7z66qtqv5/Hjh1D79694eLiAktLS7Rp0wb79+8vssxXr17F5MmTUbduXVhaWsLa2hotWrTA0qVLER8fr/O1F7R582YAwIgRI2BiYqI13YEDB9C/f3+4ubnB1NQU9vb2qF+/PoYMGYKvv/4aGRkZGvMlJibi448/Rrt27eDg4AC5XA5PT0+MGTMGISEhRZbvl19+wejRo+Hl5QULCws4OjqiefPmmDNnDi5evKgxz7Nnz/Cf//wHTZo0gbW1NaysrNCkSRMsWrQIMTExGvMU/FsRExODefPmoXbt2jA3N4ebmxtGjx5dZEvRkydPMH36dHh6eqr9PSpKeHg43nnnHTRo0ACWlpawsLCAp6cn2rdvjyVLlmg999ixYwHkDwx/8OBBkechKhVBRHqTnp4u7O3tBQBx4cIFIYQQ9+/fFzKZTJiYmIhnz56ppH/27JkwNjYWAMTRo0e1HjcoKEgAEDKZTERERKgdo127dgKA9LGzs1P5fsiQISIzM1PtuF5eXgKA+O9//ysaNGggAAgzMzPpGhTnOnPmjMrx5HK5sLa2Vtk2YsQIkZubq7H8f/zxh7CxsZHSWlhYSPltbW3FgQMHpH0Fr6+011gUXe/BW2+9JQCIt956S+uxAgMDBQDh5eWltk85/6xZswQAYWRkJJ1H8Zk4caLGY3fr1k0AEEuXLhW+vr4CgDAxMRG2trYq+ZctW1Zofj8/P6334KuvvpLus6mpqUody2QysXXrVo3HjomJEY0bN5bSmpqaStclk8nEt99+K50jMDBQ6/0r6I033hDOzs7ScZ2dnYWbm5v0eeONN6S0yj+jBSnXy+bNm4WRkZGQyWRqP0OLFy8WQgixbNkyqX4Kpvn222+1lnfZsmVCJpNJaS0tLYWZmZn0/SuvvCKuXLmi8/Ur5OXlCScnJwFA7N27V2u6yZMnq5TV2tpaWFpaqmzT9PsVEhIi3NzcpDTGxsYqv68ymUysXr1a4zlTU1PFiBEjVM5hY2Mj5HK59H2LFi3U8gUFBan87FtaWgorKyvpewcHB3Hu3Dm1fBEREVKao0ePCldXVym/8jltbW3FtWvXNJb5r7/+Eg4ODlr/Hu3fv1/r/frll19UzqP8s674aPodU6hbt64AIL755hutaYjKAgMCIj3atWuXACDq1aunsr1Lly4CgFi7dq1anv79+wsAYtSoUVqPO2XKFAFAdO3aVWV7ZmamaNOmjQAgXnvtNXHs2DGRmpoqhBAiJSVFbN++XfoPc/78+WrHVTykWVtbC3d3d3Ho0CGRlZUlhBAiKipKOlZISIgYO3asOHbsmHj27JnIy8sTQgjx/PlzsWHDBumhdMOGDWrnePnypXjllVcEAFGnTh1x+vRpKf+ff/4pWrRoofKfc8H/gEt7jUXR9R6UVUDg4OAgzMzMxLp160RiYqIQQoj4+HgxdepU6R78/vvvavkVD/QODg7Czs5ObNu2TaSlpUnlHDx4sPQQe+fOHa35CwsIHBwchIeHhzh8+LB0D8LDw0X79u2le5SQkKCWv1+/ftKD1datW0VGRoYQQohHjx6JUaNGCTMzM+nBtDgBgRCqD4CaHmYVdAkIFA/oc+fOFbGxsUKI/J9hRd0YGRmJTz/9VBgbG4tVq1ZJ1xodHS1do5WVlcZ78MUXX0gPw2vWrBFPnz4VQgiRk5MjQkNDRc+ePQUAUbNmTZGcnFysexAWFiZd2/379zWmOXfunMo1PH/+XNoXHx8vTp06Jd566y3x5MkTlXwRERHSA+3w4cPFX3/9JbKzs4UQ+YHehx9+KExMTAQA8eOPP6qdd+TIkdJ533//fREVFSWEyA9iHj9+LHbv3i1mzJihkufRo0fSORs3bizOnz8v7QsODhYNGzYUAISjo6N4/PixWnmVg4ZOnTqJy5cvCyGEyM7OFr/++qv096ZLly5q5U1KShK1atUSAEStWrXEL7/8Iv09unjxomjSpInKA37Bn7l69eoJAKJPnz7i77//lranp6eLv//+WyxfvlwEBARorCMhhBg7dmyRf++JygIDAiI96tGjhwAgVq5cqbJ98+bNAoBo1KiRWp69e/cKAMLc3Fzjg0Z6err0lnLLli0q+/z9/QUA0aRJE5GUlKSxTKGhoUImkwkzMzMRExOjsk/xIGhsbFyiN5cK33//vQAg6tatq7bvo48+kq7v7t27avvj4uJU3gIX/A+4tNdYFF3vQVkFBIU9FLdq1UoAEFOnTlXbp3igByBOnz6ttj8jI0PUqFFDABCrVq3Smr+wgEAul4tbt26p7Y+NjRXm5uYCgNi1a5fKPsWDKACxc+dOtby5ubnS74W+AwJt9zYnJ0fUrl1bSqPp/iUmJkpvsAteZ1xcnLC0tBQymUz89ttvGsuXnZ0t1e8XX3xR+EUXsHXrVinY0ObTTz+VHlSLY/jw4QKAmDBhgtY069at0/im/7fffpPuWXHeeM+YMUN6oFcETsqioqKklwyzZs1S2af889CoUSMpKFb2888/S2kUAYqC4j6ZmZmJmzdvquV9+vSp1hcUMTEx0vbo6Gidr1fZ2rVrpWCEqDxxDAGRnjx48EDqx6wYN6AwcuRIWFhYIDw8HH/88YfKPl9fX9ja2iIjIwM//PCD2nF//vlnJCYmwtzcHMOHD1fZt2XLFgDA//t//w82NjYay9WqVSs0adIEWVlZOHPmjMY0/fr1w6uvvqrztRY0cOBAAMD9+/fx9OlTlX3ff/89gPwxFfXq1VPL6+zsXOisKWV1jUUp7T3QlaenJyZOnKhx35AhQwAAN27c0Jq/U6dO6NGjh9p2uVwuDTYtLH9hhg8fjkaNGqltd3FxQYcOHTQeW1G/3t7eGDdunFpeIyMjLF26tETlKQ+LFy9W22ZsbIzXX38dAGBubo758+erpbG1tdV6D3bv3o20tDS0bt1aOk5BJiYmGDNmDADg1KlTxSpzdHQ0ABQ6Zay9vT0AIC4uTucZzV68eIFDhw4B0HxfFBQ/r9evX1fp26+YTrlJkyY6z3wkhMCBAwcAADNmzIC7u7tampo1a2LGjBkAgH379mk91oIFC2BhYaG2vX///jAzMwMA/P333yr7FMcbMWIEfHx81PK6u7tL5y7IxsYGRkb5j1kF/87pSlGHJc1PpCsGBER6EhAQACEEunTpojao1NbWFkOHDpXSKbOwsJAe9Hfu3Kl2XMU2X19f2NnZSduTk5OlB5MPP/wQ7u7uWj+3b98GkD+wV5NOnToVeX3JyclYu3YtunXrBldXV5iZmUmD+ywtLaV0T548kb7OysrCP//8AwDo1q2b1mMrDw4teM6yusai6HIPykKbNm2kh4qCatSoASD/QU2bdu3aad2nS/7ClOTYV65cAQB07dpV6yrBnTp1KnQgbEVxdHRE3bp1Ne5zc3MDADRu3BhWVlaFpnn58qXK9vPnzwMAwsLCCv0ZXblyJYDi/4zGxcVJ5demV69eMDc3x9WrV9GlSxds3boVERERhR734sWL0iD2nj17ai13kyZNpDzKZVe83Bg8eLDO1xIRESH9DPXq1Utrut69ewMAnj9/rvU6tP28mpiYwMXFBYDqz2tWVpYUIPTs2VPrubXts7CwkAK+fv36YdmyZbh06RKysrK0HqsgRR1mZ2cb/EJ7VL70/xeXyADl5eVh+/btAKD17e9bb72FvXv3Yv/+/Vi/fj2sra2lfRMnTkRAQACCg4MRGRkJLy8vAPkPAorZXQoe99mzZ9J/5ro+AKalpWnc7urqWmi+O3fu4PXXX8fjx4+lbZaWlrC3t5cebhVvDlNTU6U0L168kN5WKh4oNfHw8NC4vSyvsShF3YOyoq2VA4D00JydnV1u+cu6bIqH1cLqVy6Xw9nZGc+ePStRucqKLtdXknugeIOfnp6O9PT0IstR3J9RxcxAcrlca5o6depgy5YtmDFjBi5evCjN7OPi4oIePXpg7NixGDJkiErQpig3AK2z+hRWdkV9Kv5e6SI2Nlb6WtvvPZDfSqCcR3m2KYXi1tWLFy+k2dx0PXdBW7ZswZAhQ3D9+nV89NFH+Oijj2BmZoY2bdrA19cXU6ZMKTRwU27R0DbjE1FZYAsBkR6cOnVKelieOnWqytSIik+/fv0AACkpKVKTuULXrl3h5eUFIYTK1JH79u1DTk4O3Nzc0KdPH5U8yt0CQkJCIPLHEBX60TTlJJDfZaIwkyZNwuPHj+Ht7Y3vv/8ez58/R2pqKmJjY/Hs2TOVVgGhNP2n8tfa3h4XTFde11iUou4Baaaou8LqVzlddaT4OZ0xY4ZOP6PaptbVxsnJCYB6y0RB48aNQ2RkJDZu3IhRo0bB09MTcXFxOHDgAIYOHYpu3bohKSlJrdwWFhY6lVsIodKap6jzoupeG13zlfT45XHMWrVq4cqVKzh58iTmzp2LVq1aIS8vDxcuXMCiRYtQr149nD59Wmt+5RcbinolKg8MCIj0QNMaA4Up2G1IJpNJi9YodxtSfD1mzBi1LheK7guAej/ZshQVFSV1Ddi7dy+GDx+u9gZM25tfJycn6UFb+W1kQdr2VdQ16kJx/wt7q5eYmFhRxak0FC0rhdVvZmYmnj9/XlFFqnCKfvDl9TOqqfuLNo6Ojpg+fTr27duHR48e4d69e1i8eDFkMhnOnTunEjAryp2enq7T/PsFKfIXJ8BRbomLiorSmk65NVJx/aXl6Ogo/T1SPn5Byi84NDEyMkLfvn2xYcMGhIaG4sWLF9i9ezdq1aqFly9fYuzYsVq7ESnq0M7ODqampiW8EqKiMSAgqmBxcXH4+eefAeQvNpWcnKz18+effwIALly4oLZ4jaJL0O3bt3H58mXpX+V9yhwcHNC4cWMAhQ+8Ky3l/7S1Dbr97bffNG43MzOT+h8HBQVpPYe2fRV1jbpwcHAAUPhDzKVLlyqqOJXGa6+9BgA4e/as1jQXLlwodOG9wiiPt6isrQyK8SchISElHsNSGMXvQFxcHFJSUoqVt27dulizZo20KNavv/4q7evYsaP0prwkv18dO3YEABw5ckTnPLVr15ZeKCgWrdNE8TfFyclJY3ehkjAzM0Pz5s0BoNDJBwp7w6+JjY0Nxo4dK70YiomJ0RocKsZDaBrQTFSWGBAQVbCdO3ciOzsbdnZ2GDx4MKytrbV+2rRpI83iUrCVoEGDBtIguR07dkitA02bNtX6IP7OO+8AyP+Ptaj/0Es60FR5IPP169fV9icnJ2tcPVlBMWD6wIEDGlfnfP78OTZu3Kg1f0Vcoy5atGgBALh8+bLGoODWrVvSjC2GRFG/Dx8+xJ49e9T2CyGwevXqEh/f1tZW+rqyDsKcMGECLCwskJubi1mzZhU6y09eXl6xr6Njx44wNjZGXl4eQkNDNabJzMws9BiKvuvKXeNcXV3h6+sLAFi7di3u3LlT6DEK/n5NmTIFAPDPP//g22+/Lfwi/kcmk2HUqFEAgO+++05j62J0dDS+++47AJBmZiorinN///330kQEymJjY7X+PSpq8LDy+ABtXRAVLw0Km2SBqCwwICCqYIoHe19fX2mqu8KMGDECQP5Df8G3porpSvft2yeNJSg4hamyGTNmSEHEhAkTsHTpUpWH1bS0NAQFBWH27NlaZ1cpSuPGjVGrVi0AwOTJk/HXX39J+y5evIju3bsX2rd59uzZcHNzQ3p6Ovr27YuzZ89Kb3pDQ0PRu3fvQt8eV8Q16kIR7GVnZ2PkyJHSw0R2djZ++ukn9OrVS+vsNNVZly5dpBlhpk2bhm3btkkPp48fP8a4ceNw7tw5lZmoisPe3l4aABoYGFjiloby5O7ujk8++QQAcOzYMfTu3RsXLlyQAgMhBMLDw7Fu3To0bdoUR48eLdbxbWxs0KpVKwDaW6Fmz56NkSNH4uDBgyoDd1NSUrBx40bs2LEDADBgwACVfJ9//jmcnJyQlJSEzp07IyAgQKXrW3x8PA4dOoQ333xT7eG8R48eGD16tHT+//u//5O64gghEB0djS1btkiBg8KSJUtgb2+PFy9eoFevXipTMV+4cAG9evVCQkICHB0dC50OtSRmzpyJmjVrIjMzE/369cPvv/8u/T36888/0atXL2kig4L++OMPNG/eHF988QVu3bolpRNC4I8//pCmXq1ZsyaaNWumlj83N1f6+8mAgMpd2S5rQESFuXjxorRQzZEjR3TKc+PGDSnP4cOHVfbFx8cLMzMzab+RkZHaSp0FxcXFSaugKj62trbC3t5eyGQyaZuJiYlaXsWCVEUtFnXkyBFptVL8b8VXxcqzlpaWKgsUnTlzRi3/uXPnhLW1tUp+xff29vbSwmYANC5UVJprLIqu90AIIbZs2aJSBhsbG6m+2rdvLy2iVtjCZCVd2KywhcUU/Pz8BADRrVu3YuXX5R4UVv6nT5+KRo0aSffF1NRUWu3VyMhIbNq0SVoddu/evVrPoY1icTv8b/E0T09P4eXlpbLaqy4Lk2m6rwqF3TuFourws88+E8bGxlI5zMzMhJOTkzA1NVX5uSm4uJsuFCshd+zYsdCyKT7W1tYqK+4CEJ07dxYpKSlqea9cuSK8vb2ldDKZTDg4OKj8zgIQvXr1Usubmpoq3nzzTbXfTblcLn1fcEEzIYQICgqSFlwE8leAViz8pvi7EBwcrJZP14XqCvuZvnz5ssq9Uf57ZGNjI/bv36/xHMo/Y4qfcycnJ5W/jba2thrLLYQQp06dEgCEq6uryMzM1Fp2orLAFgKiCqToM2pnZ6c2C5A2zZo1k/qPFhyM7OTkpPIG7/XXXy90ejwgf6Gb3377DT/99BOGDx8OT09PZGZmIj09HR4eHujfvz/8/f2LPbOJskGDBiE4OBgDBw6Evb09cnJy4OzsjEmTJuHKlStaF2NS6Ny5M27cuIFJkyahRo0ayMnJgb29PSZPnowrV66ovNlXLLBU0deoiylTpuD48ePo2bMnbG1tkZOTgwYNGuCTTz7B2bNnDbKFAMh/Q3758mUsXboUDRo0gJGREUxMTDBgwACcPn0a06ZNk946a6rfoixZsgQbNmxA69atYWpqisePHyMyMlLv05gW9J///Afh4eF499130bx5c5ibmyMhIUHqLrho0SL88ccfUn/+4njrrbdgbm6OP/74Q+O8/B9++CG+/PJLvPHGG2jUqBFMTEyQkpICV1dX9O7dGwEBAQgKCtL4M/rqq6/i5s2b8Pf3R69eveDs7Izk5GTk5eWhfv36GDt2LPbt26exS5ylpSUOHjyIo0eP4o033kCNGjWQkZEBa2trNG/eHHPnzsWmTZvU8nXr1g3h4eFYsGABfHx8kJeXByEEfHx8sHDhQty6dQtdunQp9n3SRevWrXHjxg1MnToVHh4eyMnJgZ2dHd566y1cuXIFbdu21ZivTZs2OHDgAGbOnIlWrVrB2dlZWjSyZcuWWLRoUaHl3r17N4D8Wdt0aU0mKg2ZEJV01BURkRabN2/GO++8gzp16uD+/fv6Lg6Vsbt376JBgwYAgEePHsHT01PPJaqaJk+ejMDAQKxYsQLLli3Td3GoGFJTU/HKK68gLS0Nd+7cQZ06dfRdJKrm2EJARFVKRkYG1q9fDwDSWg1UvaxZswZA/ngUBgMlt2zZMsjlcvj7+6ssAEiVn7+/P5KTkzF16lQGA1QhGBAQUaWzb98+LF26FGFhYdJMHTk5OQgODkbPnj1x8+ZNmJubY968eXouKZVEeHg4pk6diuDgYCQnJ6tsnzRpEgIDAwGgzAeIGhpvb2/MmTMHcXFx+Prrr/VdHNJRSkoK/vvf/8La2horVqzQd3HIQJgUnYSIqGI9e/YMH3/8MT7++GPIZDI4ODggJSVFCg7MzMwQGBgodSuhqiUjIwNbt25VGVOTnZ2NtLQ0Kc3cuXMLnTGLdPPBBx/A2traYMerVEUPHz7ErFmz8Oqrr6ostkhUnjiGgIgqnXv37iEwMBBBQUGIjIxEfHw8TExM4OnpiR49emD+/PkMBqqw5ORkbNq0Cb/99htu376N2NhY5OTkwNXVFR06dMA777xT5MBzIiIqOwwIiIiIiIgMGMcQEBEREREZMAYEREREREQGjAEBEREREZEBY0BARERERGTAGBAQERERERkwBgRERERERAaMAQERERERkQFjQEBEREREZMD+P+7pkPAG/JLXAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"图片尺寸: 2316 x 1713 像素\n",
"图片尺寸: 2400 x 1800 像素\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x141c1bd10>"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 计算每个算法的平均路径长度和平均运行时间\n",
"avg_paths = df_results.mean()\n",
"avg_times = df_time.mean()\n",
"\n",
"# 创建散点图，设置更大的尺寸和DPI\n",
"plt.figure(figsize=(8, 6))\n",
"\n",
"# 设置坐标轴范围\n",
"x_max = max(avg_times) * 2.8\n",
"y_max = max(avg_paths) * 1.05\n",
"x_min = -3\n",
"y_min = min(avg_paths) * 0.99\n",
"plt.xlim(x_min, x_max)\n",
"plt.ylim(y_min, y_max)\n",
"\n",
"# 定义不同算法的颜色和标记\n",
"colors = ['red', 'blue', 'green', 'purple', 'orange', 'brown', 'pink']\n",
"markers = ['o', 's', '^', 'D', 'v', '<', '>']\n",
"\n",
"# 算法名称映射\n",
"algo_names = {\n",
" '贪心算法': 'Greedy',\n",
" '最近邻算法': 'NN',\n",
" '插入法': 'Insertion',\n",
" 'EoH-TSP': 'EoH',\n",
" 'AAD-TSP': 'AAE',\n",
" 'MEoH-TSP': 'MEoH'\n",
"}\n",
"\n",
"# 绘制散点和标注，增加点的大小和字体大小\n",
"for i, alg in enumerate(avg_paths.index):\n",
" x = avg_times[alg]\n",
" y = avg_paths[alg]\n",
"\n",
" # 贪心算法的点的大小要比别的大1.5倍\n",
" if alg == '贪心算法':\n",
" plt.scatter(x, y,\n",
" color=colors[i % len(colors)],\n",
" marker=markers[i % len(markers)],\n",
" s=200, # 增大点的大小\n",
" label=algo_names[alg])\n",
" else:\n",
" plt.scatter(x, y,\n",
" color=colors[i % len(colors)],\n",
" marker=markers[i % len(markers)],\n",
" s=100, # 增大点的大小\n",
" label=algo_names[alg])\n",
" \n",
" if alg == 'EoH-TSP':\n",
" xytext = (40, 30)\n",
" else:\n",
" xytext = (40, -5)\n",
"\n",
" plt.annotate(f'({x:.2f}, {y:.2e})',\n",
" xy=(x, y),\n",
" xytext=xytext,\n",
" textcoords='offset points',\n",
" fontsize=18, # 增大标注字体\n",
" bbox=dict(facecolor='white', edgecolor='none', alpha=0.7))\n",
"\n",
"plt.xlabel('Average running time (seconds)', fontsize=18)\n",
"plt.ylabel('Average path length', fontsize=18)\n",
"\n",
"# 设置更大的刻度字体大小\n",
"plt.xticks(fontsize=14)\n",
"plt.yticks(fontsize=14)\n",
"\n",
"# 设置y轴为科学计数法显示\n",
"plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\n",
"\n",
"# 添加更大的图例\n",
"plt.legend(fontsize=14, loc='upper right')\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig('tsp_pareto.png', dpi=300, bbox_inches='tight')\n",
"plt.show()\n",
"\n",
"# 输出这张图片的精准大小\n",
"import os\n",
"import matplotlib.pyplot as plt\n",
"from PIL import Image\n",
"# 打开图片\n",
"image_path = 'tsp_pareto.png'\n",
"image = Image.open(image_path)\n",
"# 获取图片的尺寸\n",
"width, height = image.size\n",
"# 输出图片的尺寸\n",
"print(f\"图片尺寸: {width} x {height} 像素\")\n",
"# 对图片进行拉长拉宽强制到2400*1800像素 \n",
"image = image.resize((2400, 1800), Image.Resampling.LANCZOS)\n",
"# 保存图片\n",
"image.save(image_path)\n",
"# 输出图片的尺寸\n",
"image = Image.open(image_path)\n",
"width, height = image.size\n",
"print(f\"图片尺寸: {width} x {height} 像素\")\n",
"# 再次展示图片\n",
"plt.imshow(image)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Greedy: (0.02, 4.01e+05)\n",
"NN: (0.02, 4.01e+05)\n",
"Insertion: (23.17, 3.66e+05)\n",
"EoH: (0.84, 4.01e+05)\n",
"AAE: (13.98, 3.46e+05)\n",
"MEoH: (0.03, 3.89e+05)\n"
]
}
],
"source": [
"# 输出上面绘图中每个散点的名称以及两个分别的指标值\n",
"for i, alg in enumerate(avg_paths.index):\n",
" x = avg_times[alg]\n",
" y = avg_paths[alg]\n",
" print(f\"{algo_names[alg]}: ({x:.2f}, {y:.2e})\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
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},
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",
"text/plain": [
"<Figure size 1200x900 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 提取EoH-TSP和AAD-TSP的运行时间数据\n",
"selected_algos = ['EoH-TSP', 'AAD-TSP']\n",
"df_selected = df_time[selected_algos]\n",
"\n",
"# 设置matplotlib支持中文显示\n",
"plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签\n",
"plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号\n",
"plt.rcParams['font.size'] = 16 # 设置更大的字号\n",
"\n",
"# 创建柱状图\n",
"plt.figure(figsize=(12, 9))\n",
"bar_width = 0.5\n",
"x = np.arange(len(df_selected.index))\n",
"\n",
"# 绘制柱状图\n",
"bars1 = plt.bar(x - bar_width/2, df_selected['EoH-TSP'], bar_width, label='AAD-TSP')\n",
"bars2 = plt.bar(x + bar_width/2, df_selected['AAD-TSP'], bar_width, label='EoH-TSP')\n",
"\n",
"# 在柱形上添加数值标注\n",
"for bars in [bars1, bars2]:\n",
" for bar in bars:\n",
" height = bar.get_height()\n",
" plt.text(bar.get_x() + bar.get_width()/2., height,\n",
" f'{height:.2f}',\n",
" ha='center', va='bottom',fontsize=16)\n",
"\n",
"# 设置图表属性\n",
"# plt.xlabel('测试实例')\n",
"# plt.title('在不同实例上的运行时间')\n",
"plt.xticks(x, df_selected.index, rotation=45,fontsize=20)\n",
"plt.legend(fontsize=20)\n",
"\n",
"# 调整布局避免标签被截断\n",
"plt.tight_layout()\n",
"plt.savefig('tsp_time.png',dpi=300)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"测试实例: GR96.tsp\n",
"\n",
"使用算法: test\n",
"执行时间:0.2226秒, 路径长度:629.57\n",
"\n",
"测试实例: XIT1083.tsp\n",
"\n",
"使用算法: test\n",
"执行时间:169.8405秒, 路径长度:4018.53\n",
"\n",
"测试实例: RBU737.tsp\n",
"\n",
"使用算法: test\n",
"执行时间:60.1185秒, 路径长度:3735.38\n",
"\n",
"测试实例: ulysses16.tsp\n",
"\n",
"使用算法: test\n",
"执行时间:0.0236秒, 路径长度:74.00\n",
"\n",
"测试实例: PBK411.tsp\n",
"\n",
"使用算法: test\n",
"执行时间:8.7138秒, 路径长度:1544.15\n",
"\n",
"测试实例: PR76.tsp\n",
"\n",
"使用算法: test\n",
"执行时间:0.1272秒, 路径长度:120233.54\n",
"\n",
"测试实例: CHN144.tsp\n",
"\n",
"使用算法: test\n",
"执行时间:0.4914秒, 路径长度:33138.13\n",
"\n",
"测试实例: eil76.tsp\n",
"\n",
"使用算法: test\n",
"执行时间:0.1449秒, 路径长度:588.83\n",
"\n",
"测试实例: eil101.tsp\n",
"\n",
"使用算法: test\n",
"执行时间:0.2218秒, 路径长度:693.38\n",
"\n",
"测试实例: ulysses8.tsp\n",
"\n",
"使用算法: test\n",
"执行时间:0.0171秒, 路径长度:37.83\n",
"\n",
"算法在各个实例上的表现:\n",
"\n",
"test:\n",
" GR96.tsp: 路径长度 = 629.57\n",
" XIT1083.tsp: 路径长度 = 4018.53\n",
" RBU737.tsp: 路径长度 = 3735.38\n",
" ulysses16.tsp: 路径长度 = 74.00\n",
" PBK411.tsp: 路径长度 = 1544.15\n",
" PR76.tsp: 路径长度 = 120233.54\n",
" CHN144.tsp: 路径长度 = 33138.13\n",
" eil76.tsp: 路径长度 = 588.83\n",
" eil101.tsp: 路径长度 = 693.38\n",
" ulysses8.tsp: 路径长度 = 37.83\n",
" GR96.tsp: 执行时间 = 0.2226秒\n",
" XIT1083.tsp: 执行时间 = 169.8405秒\n",
" RBU737.tsp: 执行时间 = 60.1185秒\n",
" ulysses16.tsp: 执行时间 = 0.0236秒\n",
" PBK411.tsp: 执行时间 = 8.7138秒\n",
" PR76.tsp: 执行时间 = 0.1272秒\n",
" CHN144.tsp: 执行时间 = 0.4914秒\n",
" eil76.tsp: 执行时间 = 0.1449秒\n",
" eil101.tsp: 执行时间 = 0.2218秒\n",
" ulysses8.tsp: 执行时间 = 0.0171秒\n"
]
}
],
"source": [
"# 用于存储算法结果\n",
"test_results = {}\n",
"test_time = {}\n",
"\n",
"# 遍历测试实例\n",
"for test_case in test_files:\n",
" print(f\"\\n测试实例: {test_case}\")\n",
" \n",
" # 加载数据\n",
" dist_matrix = load_tsp_data(test_case)\n",
" if dist_matrix is None:\n",
" continue\n",
" \n",
" # 仅测试单个算法\n",
" test_algo = {\n",
" \"test\": tsp_05 # 这里可以改成想测试的其他算法\n",
" }\n",
" \n",
" # 评估算法\n",
" for algo_name, algo_func in test_algo.items():\n",
" print(f\"\\n使用算法: {algo_name}\")\n",
" path_length, exec_time = evaluate_tsp(algo_func, dist_matrix)\n",
" \n",
" if algo_name not in test_results:\n",
" test_results[algo_name] = {}\n",
" if algo_name not in test_time:\n",
" test_time[algo_name] = {}\n",
" \n",
" test_results[algo_name][test_case] = path_length\n",
" test_time[algo_name][test_case] = exec_time\n",
"\n",
"# 打印结果\n",
"print(\"\\n算法在各个实例上的表现:\")\n",
"for algo_name, scores in test_results.items():\n",
" print(f\"\\n{algo_name}:\")\n",
" for test_case, score in scores.items():\n",
" print(f\" {test_case}: 路径长度 = {score:.2f}\")\n",
" for test_case, etime in test_time[algo_name].items():\n",
" print(f\" {test_case}: 执行时间 = {etime:.4f}秒\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "lead",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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