以下是一个基础蚁群算法实现配送路径优化的代码示例，该代码参考了引用中的思路，以实现配送路径优化。 ```python import numpy as np # 初始化参数 def initialize_parameters(num_ants, num_cities, alpha, beta, rho, Q): pheromone_matrix = np.ones((num_cities, num_cities)) return pheromone_matrix, alpha, beta, rho, Q, num_ants # 计算适应度函数（总路程） def fitness_function(path, distance_matrix): total_distance = 0 for i in range(len(path) - 1): total_distance += distance_matrix[path[i]][path[i + 1]] total_distance += distance_matrix[path[-1]][path[0]] return total_distance # 蚂蚁选择下一个城市 def select_next_city(current_city, unvisited_cities, pheromone_matrix, distance_matrix, alpha, beta): probabilities = [] for city in unvisited_cities: pheromone = pheromone_matrix[current_city][city] heuristic = 1 / distance_matrix[current_city][city] probability = (pheromone ** alpha) * (heuristic ** beta) probabilities.append(probability) probabilities = np.array(probabilities) probabilities = probabilities / probabilities.sum() next_city = np.random.choice(unvisited_cities, p=probabilities) return next_city # 蚁群算法主函数 def ant_colony_optimization(distance_matrix, num_ants, num_iterations, alpha, beta, rho, Q): num_cities = len(distance_matrix) pheromone_matrix, _, _, _, _, _ = initialize_parameters(num_ants, num_cities, alpha, beta, rho, Q) best_path = None best_distance = float('inf') for _ in range(num_iterations): all_paths = [] all_distances = [] for _ in range(num_ants): current_city = np.random.randint(num_cities) unvisited_cities = list(range(num_cities)) unvisited_cities.remove(current_city) path = [current_city] while unvisited_cities: next_city = select_next_city(current_city, unvisited_cities, pheromone_matrix, distance_matrix, alpha, beta) path.append(next_city) unvisited_cities.remove(next_city) current_city = next_city all_paths.append(path) distance = fitness_function(path, distance_matrix) all_distances.append(distance) if distance < best_distance: best_distance = distance best_path = path # 更新信息素 pheromone_matrix *= (1 - rho) for path, distance in zip(all_paths, all_distances): for i in range(len(path) - 1): pheromone_matrix[path[i]][path[i + 1]] += Q / distance pheromone_matrix[path[-1]][path[0]] += Q / distance return best_path, best_distance # 示例数据 distance_matrix = np.array([ [0, 10, 15, 20], [10, 0, 35, 25], [15, 35, 0, 30], [20, 25, 30, 0] ]) num_ants = 10 num_iterations = 100 alpha = 1 beta = 2 rho = 0.5 Q = 100 best_path, best_distance = ant_colony_optimization(distance_matrix, num_ants, num_iterations, alpha, beta, rho, Q) print("最优路径:", best_path) print("最短距离:", best_distance) ``` ### 代码说明： 1. **初始化参数**：定义了蚂蚁数量、城市数量、信息素挥发系数、信息素增加强度等参数，并初始化信息素矩阵。 2. **适应度函数**：计算路径的总路程，目标是使总路程最短。 3. **选择下一个城市**：根据信息素和启发式信息计算概率，随机选择下一个城市。 4. **蚁群算法主函数**：在多次迭代中，每只蚂蚁构建路径，计算路径的总路程，并更新信息素矩阵。最终返回最优路径和最短距离。