diff --git a/tsp_data/tsp_algo.py b/tsp_data/tsp_algo.py index 0a4d47a..e13d8a1 100644 --- a/tsp_data/tsp_algo.py +++ b/tsp_data/tsp_algo.py @@ -363,12 +363,12 @@ def tsp_05(distances): # 尝试所有可能的插入位置 for i in range(len(route)): if i == len(route) - 1: - increase = (distances[route[i]][city] + - distances[city][route[0]] - + increase = (distances[route[i]][city] + + distances[city][route[0]] - distances[route[i]][route[0]]) else: - increase = (distances[route[i]][city] + - distances[city][route[i+1]] - + increase = (distances[route[i]][city] + + distances[city][route[i+1]] - distances[route[i]][route[i+1]]) if increase < min_increase: @@ -411,3 +411,63 @@ def tsp_05(distances): best_route = two_opt(best_route, distances) return best_route + +def tsp_06(distances: np.ndarray) -> List[int]: + """ + 基于邻域矩阵的TSP求解算法 + Args: + distances: 距离矩阵 + Returns: + 访问顺序列表 + """ + import numpy as np + + def generate_neighborhood_matrix(distance_matrix): + n = len(distance_matrix) + neighborhood_matrix = np.zeros((n, n), dtype=int) + for i in range(n): + sorted_indices = np.argsort(distance_matrix[i]) + neighborhood_matrix[i] = sorted_indices + return neighborhood_matrix + + def select_next_node(current_node: int, destination_node: int, unvisited_nodes: np.ndarray, distance_matrix: np.ndarray) -> int: + """ + Design a novel algorithm to select the next node in each step. + + Args: + current_node: ID of the current node. + destination_node: ID of the destination node. + unvisited_nodes: Array of IDs of unvisited nodes. + distance_matrix: Distance matrix of nodes. + + Return: + ID of the next node to visit. + """ + current_dist = distance_matrix[current_node, unvisited_nodes] + dest_dist = distance_matrix[destination_node, unvisited_nodes] + + # Normalize distances + norm_current = current_dist / np.max(current_dist) + norm_dest = dest_dist / np.max(dest_dist) + + # Weighted score (higher weight for proximity to current node) + score = 0.7 * norm_current + 0.3 * (1 - norm_dest) + + return unvisited_nodes[np.argmin(score)] + + n = len(distances) + neighbor_matrix = generate_neighborhood_matrix(distances) + route = np.zeros(n, dtype=int) + current_node = 0 + destination_node = 0 + for i in range(1, n - 1): + near_nodes = neighbor_matrix[current_node][1:] + mask = ~np.isin(near_nodes, route[:i]) + unvisited_near_nodes = near_nodes[mask] + next_node = select_next_node(current_node, destination_node, unvisited_near_nodes, distances) + current_node = next_node + route[i] = current_node + mask = ~np.isin(np.arange(n), route[:n - 1]) + last_node = np.arange(n)[mask] + route[n - 1] = last_node[0] + return route.tolist() diff --git a/tsp_data/tsp_test.ipynb b/tsp_data/tsp_test.ipynb index 39c91ce..eeebd86 100644 --- a/tsp_data/tsp_test.ipynb +++ b/tsp_data/tsp_test.ipynb @@ -9,7 +9,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "['CHN144.tsp', 'eil101.tsp', 'eil76.tsp', 'GR96.tsp', 'PBK411.tsp', 'PR76.tsp', 'RBU737.tsp', 'ulysses16.tsp', 'ulysses8.tsp', 'XIT1083.tsp']\n" + "['GR96.tsp', 'XIT1083.tsp', 'RBU737.tsp', 'ulysses16.tsp', 'PBK411.tsp', 'PR76.tsp', 'CHN144.tsp', 'eil76.tsp', 'eil101.tsp', 'ulysses8.tsp']\n" ] } ], @@ -22,8 +22,8 @@ "from tsp_algo import *\n", "\n", "# 获取tsp_data目录下所有tsp文件\n", - "data_dir = \"C:/Users/Lenovo/Desktop/LEAD/\"\n", - "data_dir2 = \"C:/Users/Lenovo/Desktop/LEAD/tsp_data/data/\"\n", + "data_dir = \"../\"\n", + "data_dir2 = \"../tsp_data/data/\"\n", "test_files = [f for f in os.listdir(data_dir2) if f.endswith('tsp')]\n", "print(test_files)\n", "\n", @@ -88,149 +88,193 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "测试实例: CHN144.tsp\n", - "\n", - "使用算法: 贪心算法\n", - "执行时间:0.0027秒, 路径长度:35884.30\n", - "\n", - "使用算法: 最近邻算法\n", - "执行时间:0.0025秒, 路径长度:35884.30\n", - "\n", - "使用算法: 插入法\n", - "执行时间:0.3583秒, 路径长度:35048.39\n", - "\n", - "使用算法: EoH-TSP\n", - "执行时间:0.1682秒, 路径长度:35884.30\n", - "\n", - "使用算法: AAD-TSP\n", - "执行时间:0.2880秒, 路径长度:33158.55\n", - "\n", - "测试实例: eil101.tsp\n", - "\n", - "使用算法: 贪心算法\n", - "执行时间:0.0010秒, 路径长度:825.24\n", - "\n", - "使用算法: 最近邻算法\n", - "执行时间:0.0010秒, 路径长度:825.24\n", - "\n", - "使用算法: 插入法\n", - "执行时间:0.1078秒, 路径长度:702.96\n", - "\n", - "使用算法: EoH-TSP\n", - "执行时间:0.0958秒, 路径长度:847.59\n", - "\n", - "使用算法: AAD-TSP\n", - "执行时间:0.1752秒, 路径长度:687.45\n", - "\n", - "测试实例: eil76.tsp\n", - "\n", - "使用算法: 贪心算法\n", - "执行时间:0.0010秒, 路径长度:711.99\n", - "\n", - "使用算法: 最近邻算法\n", - "执行时间:0.0010秒, 路径长度:711.99\n", - "\n", - "使用算法: 插入法\n", - "执行时间:0.0435秒, 路径长度:612.39\n", - "\n", - "使用算法: EoH-TSP\n", - "执行时间:0.0683秒, 路径长度:669.24\n", - "\n", - "使用算法: AAD-TSP\n", - "执行时间:0.1143秒, 路径长度:622.71\n", "\n", "测试实例: GR96.tsp\n", "\n", "使用算法: 贪心算法\n", - "执行时间:0.0010秒, 路径长度:707.09\n", + "执行时间:0.0013秒, 路径长度:707.09\n", "\n", "使用算法: 最近邻算法\n", - "执行时间:0.0020秒, 路径长度:707.09\n", + "执行时间:0.0095秒, 路径长度:707.09\n", "\n", "使用算法: 插入法\n", - "执行时间:0.0917秒, 路径长度:651.44\n", + "执行时间:0.0993秒, 路径长度:651.44\n", "\n", "使用算法: EoH-TSP\n", - "执行时间:0.0962秒, 路径长度:707.09\n", + "执行时间:0.0772秒, 路径长度:641.82\n", "\n", "使用算法: AAD-TSP\n", - "执行时间:0.2169秒, 路径长度:623.53\n", + "执行时间:0.2481秒, 路径长度:571.02\n", "\n", - "测试实例: PBK411.tsp\n", + "使用算法: MEoH-TSP\n", + "执行时间:0.0061秒, 路径长度:573.48\n", + "\n", + "测试实例: XIT1083.tsp\n", "\n", "使用算法: 贪心算法\n", - "执行时间:0.0169秒, 路径长度:1838.48\n", + "执行时间:0.0971秒, 路径长度:4584.27\n", "\n", "使用算法: 最近邻算法\n", - "执行时间:0.0170秒, 路径长度:1838.48\n", + "执行时间:0.1277秒, 路径长度:4584.27\n", "\n", "使用算法: 插入法\n", - "执行时间:7.6995秒, 路径长度:1602.35\n", + "执行时间:121.7319秒, 路径长度:4328.99\n", "\n", "使用算法: EoH-TSP\n", - "执行时间:0.9340秒, 路径长度:1835.85\n", + "执行时间:4.1403秒, 路径长度:4673.87\n", "\n", "使用算法: AAD-TSP\n", - "执行时间:5.2911秒, 路径长度:1554.96\n", + "执行时间:80.2114秒, 路径长度:4051.52\n", "\n", - "测试实例: PR76.tsp\n", - "\n", - "使用算法: 贪心算法\n", - "执行时间:0.0010秒, 路径长度:153461.92\n", - "\n", - "使用算法: 最近邻算法\n", - "执行时间:0.0000秒, 路径长度:153461.92\n", - "\n", - "使用算法: 插入法\n", - "执行时间:0.0439秒, 路径长度:125936.21\n", - "\n", - "使用算法: EoH-TSP\n", - "执行时间:0.0671秒, 路径长度:145300.57\n", - "\n", - "使用算法: AAD-TSP\n", - "执行时间:0.1271秒, 路径长度:111623.46\n", + "使用算法: MEoH-TSP\n", + "执行时间:0.1036秒, 路径长度:4397.83\n", "\n", "测试实例: RBU737.tsp\n", "\n", "使用算法: 贪心算法\n", - "执行时间:0.0544秒, 路径长度:4416.15\n", + "执行时间:0.0504秒, 路径长度:4416.15\n", "\n", "使用算法: 最近邻算法\n", - "执行时间:0.0544秒, 路径长度:4416.15\n", + "执行时间:0.0444秒, 路径长度:4416.15\n", "\n", "使用算法: 插入法\n", - "执行时间:44.8784秒, 路径长度:4097.20\n", + "执行时间:40.5620秒, 路径长度:4097.20\n", "\n", "使用算法: EoH-TSP\n", - "执行时间:2.6006秒, 路径长度:4436.28\n", + "执行时间:1.9295秒, 路径长度:4436.28\n", "\n", "使用算法: AAD-TSP\n", - "执行时间:29.6802秒, 路径长度:3870.50\n", + "执行时间:26.4296秒, 路径长度:3870.50\n", + "\n", + "使用算法: MEoH-TSP\n", + "执行时间:0.0562秒, 路径长度:4101.04\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", + "执行时间:0.0001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", + "执行时间:0.0001秒, 路径长度:104.73\n", "\n", "使用算法: 插入法\n", - "执行时间:0.0015秒, 路径长度:79.39\n", + "执行时间:0.0005秒, 路径长度:79.39\n", "\n", "使用算法: EoH-TSP\n", - "执行时间:0.0131秒, 路径长度:83.79\n", + "执行时间:0.0114秒, 路径长度:88.25\n", "\n", "使用算法: AAD-TSP\n", - "执行时间:0.0314秒, 路径长度:74.00\n", + "执行时间:0.0243秒, 路径长度:74.14\n", + "\n", + "使用算法: MEoH-TSP\n", + "执行时间:0.0011秒, 路径长度:104.65\n", + "\n", + "测试实例: PBK411.tsp\n", + "\n", + "使用算法: 贪心算法\n", + "执行时间:0.0146秒, 路径长度:1838.48\n", + "\n", + "使用算法: 最近邻算法\n", + "执行时间:0.0171秒, 路径长度:1838.48\n", + "\n", + "使用算法: 插入法\n", + "执行时间:6.5032秒, 路径长度:1602.35\n", + "\n", + "使用算法: EoH-TSP\n", + "执行时间:0.7277秒, 路径长度:1835.85\n", + "\n", + "使用算法: AAD-TSP\n", + "执行时间:4.4427秒, 路径长度:1554.96\n", + "\n", + "使用算法: MEoH-TSP\n", + "执行时间:0.0255秒, 路径长度:1776.60\n", + "\n", + "测试实例: PR76.tsp\n", + "\n", + "使用算法: 贪心算法\n", + "执行时间:0.0007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:0.0446秒, 路径长度:125936.21\n", + "\n", + "使用算法: EoH-TSP\n", + "执行时间:0.0522秒, 路径长度:153461.92\n", + "\n", + "使用算法: AAD-TSP\n", + "执行时间:0.1132秒, 路径长度:112991.57\n", + "\n", + "使用算法: MEoH-TSP\n", + "执行时间:0.0038秒, 路径长度:123787.14\n", + "\n", + "测试实例: CHN144.tsp\n", + "\n", + "使用算法: 贪心算法\n", + "执行时间:0.0019秒, 路径长度:35884.30\n", + "\n", + "使用算法: 最近邻算法\n", + "执行时间:0.0024秒, 路径长度:35884.30\n", + "\n", + "使用算法: 插入法\n", + "执行时间:0.2761秒, 路径长度:35048.39\n", + "\n", + "使用算法: EoH-TSP\n", + "执行时间:0.1257秒, 路径长度:35884.30\n", + "\n", + "使用算法: AAD-TSP\n", + "执行时间:0.2748秒, 路径长度:32744.18\n", + "\n", + "使用算法: MEoH-TSP\n", + "执行时间:0.0072秒, 路径长度:35658.61\n", + "\n", + "测试实例: eil76.tsp\n", + "\n", + "使用算法: 贪心算法\n", + "执行时间:0.0008秒, 路径长度:711.99\n", + "\n", + "使用算法: 最近邻算法\n", + "执行时间:0.0005秒, 路径长度:711.99\n", + "\n", + "使用算法: 插入法\n", + "执行时间:0.0396秒, 路径长度:612.39\n", + "\n", + "使用算法: EoH-TSP\n", + "执行时间:0.0517秒, 路径长度:669.24\n", + "\n", + "使用算法: AAD-TSP\n", + "执行时间:0.1012秒, 路径长度:622.71\n", + "\n", + "使用算法: MEoH-TSP\n", + "执行时间:0.0043秒, 路径长度:577.27\n", + "\n", + "测试实例: eil101.tsp\n", + "\n", + "使用算法: 贪心算法\n", + "执行时间:0.0006秒, 路径长度:825.24\n", + "\n", + "使用算法: 最近邻算法\n", + "执行时间:0.0007秒, 路径长度:825.24\n", + "\n", + "使用算法: 插入法\n", + "执行时间:0.0900秒, 路径长度:702.96\n", + "\n", + "使用算法: EoH-TSP\n", + "执行时间:0.0815秒, 路径长度:847.59\n", + "\n", + "使用算法: AAD-TSP\n", + "执行时间:0.1857秒, 路径长度:702.70\n", + "\n", + "使用算法: MEoH-TSP\n", + "执行时间:0.0053秒, 路径长度:720.41\n", "\n", "测试实例: ulysses8.tsp\n", "\n", @@ -241,168 +285,176 @@ "执行时间:0.0000秒, 路径长度:38.48\n", "\n", "使用算法: 插入法\n", - "执行时间:0.0000秒, 路径长度:37.83\n", + "执行时间:0.0001秒, 路径长度:37.83\n", "\n", "使用算法: EoH-TSP\n", - "执行时间:0.0076秒, 路径长度:38.48\n", + "执行时间:0.0065秒, 路径长度:38.11\n", "\n", "使用算法: AAD-TSP\n", - "执行时间:0.0216秒, 路径长度:37.83\n", + "执行时间:0.0169秒, 路径长度:37.83\n", "\n", - "测试实例: XIT1083.tsp\n", - "\n", - "使用算法: 贪心算法\n", - "执行时间:0.1163秒, 路径长度:4584.27\n", - "\n", - "使用算法: 最近邻算法\n", - "执行时间:0.1150秒, 路径长度:4584.27\n", - "\n", - "使用算法: 插入法\n", - "执行时间:146.8186秒, 路径长度:4328.99\n", - "\n", - "使用算法: EoH-TSP\n", - "执行时间:5.4694秒, 路径长度:4673.87\n", - "\n", - "使用算法: AAD-TSP\n", - "执行时间:89.6922秒, 路径长度:4051.52\n", + "使用算法: MEoH-TSP\n", + "执行时间:0.0003秒, 路径长度:38.48\n", "\n", "所有算法在各个实例上的表现:\n", "\n", "贪心算法:\n", - " CHN144.tsp: 路径长度 = 35884.30\n", - " eil101.tsp: 路径长度 = 825.24\n", - " eil76.tsp: 路径长度 = 711.99\n", " GR96.tsp: 路径长度 = 707.09\n", - " PBK411.tsp: 路径长度 = 1838.48\n", - " PR76.tsp: 路径长度 = 153461.92\n", + " XIT1083.tsp: 路径长度 = 4584.27\n", " RBU737.tsp: 路径长度 = 4416.15\n", " ulysses16.tsp: 路径长度 = 104.73\n", + " PBK411.tsp: 路径长度 = 1838.48\n", + " PR76.tsp: 路径长度 = 153461.92\n", + " CHN144.tsp: 路径长度 = 35884.30\n", + " eil76.tsp: 路径长度 = 711.99\n", + " eil101.tsp: 路径长度 = 825.24\n", " ulysses8.tsp: 路径长度 = 38.48\n", - " XIT1083.tsp: 路径长度 = 4584.27\n", - " CHN144.tsp: 执行时间 = 0.0027秒\n", - " eil101.tsp: 执行时间 = 0.0010秒\n", - " eil76.tsp: 执行时间 = 0.0010秒\n", - " GR96.tsp: 执行时间 = 0.0010秒\n", - " PBK411.tsp: 执行时间 = 0.0169秒\n", - " PR76.tsp: 执行时间 = 0.0010秒\n", - " RBU737.tsp: 执行时间 = 0.0544秒\n", - " ulysses16.tsp: 执行时间 = 0.0000秒\n", + " GR96.tsp: 执行时间 = 0.0013秒\n", + " XIT1083.tsp: 执行时间 = 0.0971秒\n", + " RBU737.tsp: 执行时间 = 0.0504秒\n", + " ulysses16.tsp: 执行时间 = 0.0001秒\n", + " PBK411.tsp: 执行时间 = 0.0146秒\n", + " PR76.tsp: 执行时间 = 0.0007秒\n", + " CHN144.tsp: 执行时间 = 0.0019秒\n", + " eil76.tsp: 执行时间 = 0.0008秒\n", + " eil101.tsp: 执行时间 = 0.0006秒\n", " ulysses8.tsp: 执行时间 = 0.0000秒\n", - " XIT1083.tsp: 执行时间 = 0.1163秒\n", "\n", "最近邻算法:\n", - " CHN144.tsp: 路径长度 = 35884.30\n", - " eil101.tsp: 路径长度 = 825.24\n", - " eil76.tsp: 路径长度 = 711.99\n", " GR96.tsp: 路径长度 = 707.09\n", - " PBK411.tsp: 路径长度 = 1838.48\n", - " PR76.tsp: 路径长度 = 153461.92\n", + " XIT1083.tsp: 路径长度 = 4584.27\n", " RBU737.tsp: 路径长度 = 4416.15\n", " ulysses16.tsp: 路径长度 = 104.73\n", + " PBK411.tsp: 路径长度 = 1838.48\n", + " PR76.tsp: 路径长度 = 153461.92\n", + " CHN144.tsp: 路径长度 = 35884.30\n", + " eil76.tsp: 路径长度 = 711.99\n", + " eil101.tsp: 路径长度 = 825.24\n", " ulysses8.tsp: 路径长度 = 38.48\n", - " XIT1083.tsp: 路径长度 = 4584.27\n", - " CHN144.tsp: 执行时间 = 0.0025秒\n", - " eil101.tsp: 执行时间 = 0.0010秒\n", - " eil76.tsp: 执行时间 = 0.0010秒\n", - " GR96.tsp: 执行时间 = 0.0020秒\n", - " PBK411.tsp: 执行时间 = 0.0170秒\n", - " PR76.tsp: 执行时间 = 0.0000秒\n", - " RBU737.tsp: 执行时间 = 0.0544秒\n", - " ulysses16.tsp: 执行时间 = 0.0000秒\n", + " GR96.tsp: 执行时间 = 0.0095秒\n", + " XIT1083.tsp: 执行时间 = 0.1277秒\n", + " RBU737.tsp: 执行时间 = 0.0444秒\n", + " ulysses16.tsp: 执行时间 = 0.0001秒\n", + " PBK411.tsp: 执行时间 = 0.0171秒\n", + " PR76.tsp: 执行时间 = 0.0007秒\n", + " CHN144.tsp: 执行时间 = 0.0024秒\n", + " eil76.tsp: 执行时间 = 0.0005秒\n", + " eil101.tsp: 执行时间 = 0.0007秒\n", " ulysses8.tsp: 执行时间 = 0.0000秒\n", - " XIT1083.tsp: 执行时间 = 0.1150秒\n", "\n", "插入法:\n", - " CHN144.tsp: 路径长度 = 35048.39\n", - " eil101.tsp: 路径长度 = 702.96\n", - " eil76.tsp: 路径长度 = 612.39\n", " GR96.tsp: 路径长度 = 651.44\n", - " PBK411.tsp: 路径长度 = 1602.35\n", - " PR76.tsp: 路径长度 = 125936.21\n", + " XIT1083.tsp: 路径长度 = 4328.99\n", " RBU737.tsp: 路径长度 = 4097.20\n", " ulysses16.tsp: 路径长度 = 79.39\n", + " PBK411.tsp: 路径长度 = 1602.35\n", + " PR76.tsp: 路径长度 = 125936.21\n", + " CHN144.tsp: 路径长度 = 35048.39\n", + " eil76.tsp: 路径长度 = 612.39\n", + " eil101.tsp: 路径长度 = 702.96\n", " ulysses8.tsp: 路径长度 = 37.83\n", - " XIT1083.tsp: 路径长度 = 4328.99\n", - " CHN144.tsp: 执行时间 = 0.3583秒\n", - " eil101.tsp: 执行时间 = 0.1078秒\n", - " eil76.tsp: 执行时间 = 0.0435秒\n", - " GR96.tsp: 执行时间 = 0.0917秒\n", - " PBK411.tsp: 执行时间 = 7.6995秒\n", - " PR76.tsp: 执行时间 = 0.0439秒\n", - " RBU737.tsp: 执行时间 = 44.8784秒\n", - " ulysses16.tsp: 执行时间 = 0.0015秒\n", - " ulysses8.tsp: 执行时间 = 0.0000秒\n", - " XIT1083.tsp: 执行时间 = 146.8186秒\n", + " GR96.tsp: 执行时间 = 0.0993秒\n", + " XIT1083.tsp: 执行时间 = 121.7319秒\n", + " RBU737.tsp: 执行时间 = 40.5620秒\n", + " ulysses16.tsp: 执行时间 = 0.0005秒\n", + " PBK411.tsp: 执行时间 = 6.5032秒\n", + " PR76.tsp: 执行时间 = 0.0446秒\n", + " CHN144.tsp: 执行时间 = 0.2761秒\n", + " eil76.tsp: 执行时间 = 0.0396秒\n", + " eil101.tsp: 执行时间 = 0.0900秒\n", + " ulysses8.tsp: 执行时间 = 0.0001秒\n", "\n", "EoH-TSP:\n", - " CHN144.tsp: 路径长度 = 35884.30\n", - " eil101.tsp: 路径长度 = 847.59\n", - " eil76.tsp: 路径长度 = 669.24\n", - " GR96.tsp: 路径长度 = 707.09\n", - " PBK411.tsp: 路径长度 = 1835.85\n", - " PR76.tsp: 路径长度 = 145300.57\n", - " RBU737.tsp: 路径长度 = 4436.28\n", - " ulysses16.tsp: 路径长度 = 83.79\n", - " ulysses8.tsp: 路径长度 = 38.48\n", + " GR96.tsp: 路径长度 = 641.82\n", " XIT1083.tsp: 路径长度 = 4673.87\n", - " CHN144.tsp: 执行时间 = 0.1682秒\n", - " eil101.tsp: 执行时间 = 0.0958秒\n", - " eil76.tsp: 执行时间 = 0.0683秒\n", - " GR96.tsp: 执行时间 = 0.0962秒\n", - " PBK411.tsp: 执行时间 = 0.9340秒\n", - " PR76.tsp: 执行时间 = 0.0671秒\n", - " RBU737.tsp: 执行时间 = 2.6006秒\n", - " ulysses16.tsp: 执行时间 = 0.0131秒\n", - " ulysses8.tsp: 执行时间 = 0.0076秒\n", - " XIT1083.tsp: 执行时间 = 5.4694秒\n", + " RBU737.tsp: 路径长度 = 4436.28\n", + " ulysses16.tsp: 路径长度 = 88.25\n", + " PBK411.tsp: 路径长度 = 1835.85\n", + " PR76.tsp: 路径长度 = 153461.92\n", + " CHN144.tsp: 路径长度 = 35884.30\n", + " eil76.tsp: 路径长度 = 669.24\n", + " eil101.tsp: 路径长度 = 847.59\n", + " ulysses8.tsp: 路径长度 = 38.11\n", + " GR96.tsp: 执行时间 = 0.0772秒\n", + " XIT1083.tsp: 执行时间 = 4.1403秒\n", + " RBU737.tsp: 执行时间 = 1.9295秒\n", + " ulysses16.tsp: 执行时间 = 0.0114秒\n", + " PBK411.tsp: 执行时间 = 0.7277秒\n", + " PR76.tsp: 执行时间 = 0.0522秒\n", + " CHN144.tsp: 执行时间 = 0.1257秒\n", + " eil76.tsp: 执行时间 = 0.0517秒\n", + " eil101.tsp: 执行时间 = 0.0815秒\n", + " ulysses8.tsp: 执行时间 = 0.0065秒\n", "\n", "AAD-TSP:\n", - " CHN144.tsp: 路径长度 = 33158.55\n", - " eil101.tsp: 路径长度 = 687.45\n", - " eil76.tsp: 路径长度 = 622.71\n", - " GR96.tsp: 路径长度 = 623.53\n", - " PBK411.tsp: 路径长度 = 1554.96\n", - " PR76.tsp: 路径长度 = 111623.46\n", - " RBU737.tsp: 路径长度 = 3870.50\n", - " ulysses16.tsp: 路径长度 = 74.00\n", - " ulysses8.tsp: 路径长度 = 37.83\n", + " GR96.tsp: 路径长度 = 571.02\n", " XIT1083.tsp: 路径长度 = 4051.52\n", - " CHN144.tsp: 执行时间 = 0.2880秒\n", - " eil101.tsp: 执行时间 = 0.1752秒\n", - " eil76.tsp: 执行时间 = 0.1143秒\n", - " GR96.tsp: 执行时间 = 0.2169秒\n", - " PBK411.tsp: 执行时间 = 5.2911秒\n", - " PR76.tsp: 执行时间 = 0.1271秒\n", - " RBU737.tsp: 执行时间 = 29.6802秒\n", - " ulysses16.tsp: 执行时间 = 0.0314秒\n", - " ulysses8.tsp: 执行时间 = 0.0216秒\n", - " XIT1083.tsp: 执行时间 = 89.6922秒\n", + " RBU737.tsp: 路径长度 = 3870.50\n", + " ulysses16.tsp: 路径长度 = 74.14\n", + " PBK411.tsp: 路径长度 = 1554.96\n", + " PR76.tsp: 路径长度 = 112991.57\n", + " CHN144.tsp: 路径长度 = 32744.18\n", + " eil76.tsp: 路径长度 = 622.71\n", + " eil101.tsp: 路径长度 = 702.70\n", + " ulysses8.tsp: 路径长度 = 37.83\n", + " GR96.tsp: 执行时间 = 0.2481秒\n", + " XIT1083.tsp: 执行时间 = 80.2114秒\n", + " RBU737.tsp: 执行时间 = 26.4296秒\n", + " ulysses16.tsp: 执行时间 = 0.0243秒\n", + " PBK411.tsp: 执行时间 = 4.4427秒\n", + " PR76.tsp: 执行时间 = 0.1132秒\n", + " CHN144.tsp: 执行时间 = 0.2748秒\n", + " eil76.tsp: 执行时间 = 0.1012秒\n", + " eil101.tsp: 执行时间 = 0.1857秒\n", + " ulysses8.tsp: 执行时间 = 0.0169秒\n", + "\n", + "MEoH-TSP:\n", + " GR96.tsp: 路径长度 = 573.48\n", + " XIT1083.tsp: 路径长度 = 4397.83\n", + " RBU737.tsp: 路径长度 = 4101.04\n", + " ulysses16.tsp: 路径长度 = 104.65\n", + " PBK411.tsp: 路径长度 = 1776.60\n", + " PR76.tsp: 路径长度 = 123787.14\n", + " CHN144.tsp: 路径长度 = 35658.61\n", + " eil76.tsp: 路径长度 = 577.27\n", + " eil101.tsp: 路径长度 = 720.41\n", + " ulysses8.tsp: 路径长度 = 38.48\n", + " GR96.tsp: 执行时间 = 0.0061秒\n", + " XIT1083.tsp: 执行时间 = 0.1036秒\n", + " RBU737.tsp: 执行时间 = 0.0562秒\n", + " ulysses16.tsp: 执行时间 = 0.0011秒\n", + " PBK411.tsp: 执行时间 = 0.0255秒\n", + " PR76.tsp: 执行时间 = 0.0038秒\n", + " CHN144.tsp: 执行时间 = 0.0072秒\n", + " eil76.tsp: 执行时间 = 0.0043秒\n", + " eil101.tsp: 执行时间 = 0.0053秒\n", + " ulysses8.tsp: 执行时间 = 0.0003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\n", - "CHN144 35884.30 35884.30 35048.39 35884.30 33158.55\n", - "eil101 825.24 825.24 702.96 847.59 687.45\n", - "eil76 711.99 711.99 612.39 669.24 622.71\n", - "GR96 707.09 707.09 651.44 707.09 623.53\n", - "PBK411 1838.48 1838.48 1602.35 1835.85 1554.96\n", - "PR76 153461.92 153461.92 125936.21 145300.57 111623.46\n", - "RBU737 4416.15 4416.15 4097.20 4436.28 3870.50\n", - "ulysses16 104.73 104.73 79.39 83.79 74.00\n", - "ulysses8 38.48 38.48 37.83 38.48 37.83\n", - "XIT1083 4584.27 4584.27 4328.99 4673.87 4051.52\n", + " 贪心算法 最近邻算法 插入法 EoH-TSP AAD-TSP MEoH-TSP\n", + "GR96 707.09 707.09 651.44 641.82 571.02 573.48\n", + "XIT1083 4584.27 4584.27 4328.99 4673.87 4051.52 4397.83\n", + "RBU737 4416.15 4416.15 4097.20 4436.28 3870.50 4101.04\n", + "ulysses16 104.73 104.73 79.39 88.25 74.14 104.65\n", + "PBK411 1838.48 1838.48 1602.35 1835.85 1554.96 1776.60\n", + "PR76 153461.92 153461.92 125936.21 153461.92 112991.57 123787.14\n", + "CHN144 35884.30 35884.30 35048.39 35884.30 32744.18 35658.61\n", + "eil76 711.99 711.99 612.39 669.24 622.71 577.27\n", + "eil101 825.24 825.24 702.96 847.59 702.70 720.41\n", + "ulysses8 38.48 38.48 37.83 38.11 37.83 38.48\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\n", - "CHN144 0.0027 0.0025 0.3583 0.1682 0.2880\n", - "eil101 0.0010 0.0010 0.1078 0.0958 0.1752\n", - "eil76 0.0010 0.0010 0.0435 0.0683 0.1143\n", - "GR96 0.0010 0.0020 0.0917 0.0962 0.2169\n", - "PBK411 0.0169 0.0170 7.6995 0.9340 5.2911\n", - "PR76 0.0010 0.0000 0.0439 0.0671 0.1271\n", - "RBU737 0.0544 0.0544 44.8784 2.6006 29.6802\n", - "ulysses16 0.0000 0.0000 0.0015 0.0131 0.0314\n", - "ulysses8 0.0000 0.0000 0.0000 0.0076 0.0216\n", - "XIT1083 0.1163 0.1150 146.8186 5.4694 89.6922\n" + " 贪心算法 最近邻算法 插入法 EoH-TSP AAD-TSP MEoH-TSP\n", + "GR96 0.0013 0.0095 0.0993 0.0772 0.2481 0.0061\n", + "XIT1083 0.0971 0.1277 121.7319 4.1403 80.2114 0.1036\n", + "RBU737 0.0504 0.0444 40.5620 1.9295 26.4296 0.0562\n", + "ulysses16 0.0001 0.0001 0.0005 0.0114 0.0243 0.0011\n", + "PBK411 0.0146 0.0171 6.5032 0.7277 4.4427 0.0255\n", + "PR76 0.0007 0.0007 0.0446 0.0522 0.1132 0.0038\n", + "CHN144 0.0019 0.0024 0.2761 0.1257 0.2748 0.0072\n", + "eil76 0.0008 0.0005 0.0396 0.0517 0.1012 0.0043\n", + "eil101 0.0006 0.0007 0.0900 0.0815 0.1857 0.0053\n", + "ulysses8 0.0000 0.0000 0.0001 0.0065 0.0169 0.0003\n" ] } ], @@ -429,6 +481,7 @@ " # \"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", @@ -486,7 +539,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -495,30 +548,30 @@ "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\n", - "ulysses8 38.48 38.48 37.83 38.48 37.83\n", - "ulysses16 104.73 104.73 79.39 83.79 74.00\n", - "eil76 711.99 711.99 612.39 669.24 622.71\n", - "PR76 153461.92 153461.92 125936.21 145300.57 111623.46\n", - "GR96 707.09 707.09 651.44 707.09 623.53\n", - "eil101 825.24 825.24 702.96 847.59 687.45\n", - "CHN144 35884.30 35884.30 35048.39 35884.30 33158.55\n", - "PBK411 1838.48 1838.48 1602.35 1835.85 1554.96\n", - "RBU737 4416.15 4416.15 4097.20 4436.28 3870.50\n", - "XIT1083 4584.27 4584.27 4328.99 4673.87 4051.52\n", + " 贪心算法 最近邻算法 插入法 EoH-TSP AAD-TSP MEoH-TSP\n", + "ulysses8 38.48 38.48 37.83 38.11 37.83 38.48\n", + "ulysses16 104.73 104.73 79.39 88.25 74.14 104.65\n", + "PR76 153461.92 153461.92 125936.21 153461.92 112991.57 123787.14\n", + "eil76 711.99 711.99 612.39 669.24 622.71 577.27\n", + "GR96 707.09 707.09 651.44 641.82 571.02 573.48\n", + "eil101 825.24 825.24 702.96 847.59 702.70 720.41\n", + "CHN144 35884.30 35884.30 35048.39 35884.30 32744.18 35658.61\n", + "PBK411 1838.48 1838.48 1602.35 1835.85 1554.96 1776.60\n", + "RBU737 4416.15 4416.15 4097.20 4436.28 3870.50 4101.04\n", + "XIT1083 4584.27 4584.27 4328.99 4673.87 4051.52 4397.83\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\n", - "ulysses8 0.0000 0.0000 0.0000 0.0076 0.0216\n", - "ulysses16 0.0000 0.0000 0.0015 0.0131 0.0314\n", - "eil76 0.0010 0.0010 0.0435 0.0683 0.1143\n", - "PR76 0.0010 0.0000 0.0439 0.0671 0.1271\n", - "GR96 0.0010 0.0020 0.0917 0.0962 0.2169\n", - "eil101 0.0010 0.0010 0.1078 0.0958 0.1752\n", - "CHN144 0.0027 0.0025 0.3583 0.1682 0.2880\n", - "PBK411 0.0169 0.0170 7.6995 0.9340 5.2911\n", - "RBU737 0.0544 0.0544 44.8784 2.6006 29.6802\n", - "XIT1083 0.1163 0.1150 146.8186 5.4694 89.6922\n" + " 贪心算法 最近邻算法 插入法 EoH-TSP AAD-TSP MEoH-TSP\n", + "ulysses8 0.0000 0.0000 0.0001 0.0065 0.0169 0.0003\n", + "ulysses16 0.0001 0.0001 0.0005 0.0114 0.0243 0.0011\n", + "PR76 0.0007 0.0007 0.0446 0.0522 0.1132 0.0038\n", + "eil76 0.0008 0.0005 0.0396 0.0517 0.1012 0.0043\n", + "GR96 0.0013 0.0095 0.0993 0.0772 0.2481 0.0061\n", + "eil101 0.0006 0.0007 0.0900 0.0815 0.1857 0.0053\n", + "CHN144 0.0019 0.0024 0.2761 0.1257 0.2748 0.0072\n", + "PBK411 0.0146 0.0171 6.5032 0.7277 4.4427 0.0255\n", + "RBU737 0.0504 0.0444 40.5620 1.9295 26.4296 0.0562\n", + "XIT1083 0.0971 0.1277 121.7319 4.1403 80.2114 0.1036\n" ] } ], @@ -544,9 +597,348 @@ "execution_count": 4, "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 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", 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", "text/plain": [ "
" ] @@ -596,61 +988,87 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "测试实例: CHN144.tsp\n", - "\n", - "使用算法: test\n", - "执行时间:0.6112秒, 路径长度:33147.39\n", - "\n", - "测试实例: eil101.tsp\n", - "\n", - "使用算法: test\n", - "执行时间:0.2688秒, 路径长度:693.38\n", - "\n", - "测试实例: eil76.tsp\n", - "\n", - "使用算法: test\n", - "执行时间:0.1627秒, 路径长度:588.83\n", "\n", "测试实例: GR96.tsp\n", "\n", "使用算法: test\n", - "执行时间:0.2340秒, 路径长度:629.57\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:10.3389秒, 路径长度:1544.15\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:0.1454秒, 路径长度:120233.54\n", + "执行时间:0.1272秒, 路径长度:120233.54\n", "\n", - "测试实例: RBU737.tsp\n", + "测试实例: CHN144.tsp\n", "\n", - "使用算法: test\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 22\u001b[39m\n\u001b[32m 20\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m algo_name, algo_func \u001b[38;5;129;01min\u001b[39;00m test_algo.items():\n\u001b[32m 21\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m使用算法: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00malgo_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m22\u001b[39m path_length, exec_time = \u001b[43mevaluate_tsp\u001b[49m\u001b[43m(\u001b[49m\u001b[43malgo_func\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdist_matrix\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 24\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m algo_name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m test_results:\n\u001b[32m 25\u001b[39m test_results[algo_name] = {}\n", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 20\u001b[39m, in \u001b[36mevaluate_tsp\u001b[39m\u001b[34m(tsp_func, distances)\u001b[39m\n\u001b[32m 17\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 18\u001b[39m \u001b[38;5;66;03m# 计时并执行TSP\u001b[39;00m\n\u001b[32m 19\u001b[39m start_time = time.time()\n\u001b[32m---> \u001b[39m\u001b[32m20\u001b[39m path = \u001b[43mtsp_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdistances\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 21\u001b[39m end_time = time.time()\n\u001b[32m 22\u001b[39m execution_time = end_time - start_time\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Lenovo\\Desktop\\LEAD\\tsp_data\\tsp_algo.py:411\u001b[39m, in \u001b[36mtsp_05\u001b[39m\u001b[34m(distances)\u001b[39m\n\u001b[32m 408\u001b[39m temp *= cooling\n\u001b[32m 410\u001b[39m \u001b[38;5;66;03m# 2-opt局部优化\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m411\u001b[39m best_route = \u001b[43mtwo_opt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbest_route\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdistances\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 413\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m best_route\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Lenovo\\Desktop\\LEAD\\tsp_data\\tsp_algo.py:277\u001b[39m, in \u001b[36mtwo_opt\u001b[39m\u001b[34m(route, distances)\u001b[39m\n\u001b[32m 273\u001b[39m old_dist = (distances[route[i-\u001b[32m1\u001b[39m]][route[i]] + \n\u001b[32m 274\u001b[39m distances[route[j-\u001b[32m1\u001b[39m]][route[j]])\n\u001b[32m 275\u001b[39m new_dist = (distances[route[i-\u001b[32m1\u001b[39m]][route[j-\u001b[32m1\u001b[39m]] + \n\u001b[32m 276\u001b[39m distances[route[i]][route[j]])\n\u001b[32m--> \u001b[39m\u001b[32m277\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m new_dist < old_dist:\n\u001b[32m 278\u001b[39m route[i:j] = \u001b[38;5;28mreversed\u001b[39m(route[i:j])\n\u001b[32m 279\u001b[39m improved = \u001b[38;5;28;01mTrue\u001b[39;00m\n", - "\u001b[31mKeyboardInterrupt\u001b[39m: " + "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" ] } ], diff --git a/tsp_data/tsp_time.png b/tsp_data/tsp_time.png index d8a040a..948052d 100644 Binary files a/tsp_data/tsp_time.png and b/tsp_data/tsp_time.png differ