tsp问题对比meoh结果

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] + 
+                    increase = (distances[route[i]][city] +
-                              distances[city][route[0]] - 
+                              distances[city][route[0]] -
                               distances[route[i]][route[0]])
                 else:
-                    increase = (distances[route[i]][city] + 
+                    increase = (distances[route[i]][city] +
-                              distances[city][route[i+1]] - 
+                              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()