蚁群算法解决非对称TSP问题的代码是什么

以下是使用蚁群算法解决非对称TSP问题的 Python 代码示例：

import numpy as np

# 非对称TSP问题的距离矩阵，例如：
# distances = np.array([[0, 1, 2, 3], [4, 0, 5, 6], [7, 8, 0, 9], [10, 11, 12, 0]])
distances = ...

# 蚂蚁数量
num_ants = 10

# 信息素挥发因子
evaporation_rate = 0.5

# 信息素增加因子
pheromone_factor = 0.1

# 迭代次数
num_iterations = 100

# 初始化信息素矩阵
pheromone_matrix = np.ones_like(distances) / (len(distances) * 10)

# 初始化最佳路径和最佳路径长度
best_path = None
best_path_length = np.inf

# 开始迭代
for iteration in range(num_iterations):
    # 初始化所有蚂蚁的位置为起点
    ant_positions = np.zeros(num_ants, dtype=int)

    # 初始化所有蚂蚁的路径长度为0
    ant_path_lengths = np.zeros(num_ants)

    # 让每只蚂蚁选择下一个节点，直到所有节点都被访问
    for i in range(len(distances) - 1):
        # 计算所有蚂蚁在当前位置时，到其他未访问节点的距离的倒数的和
        probabilities = np.zeros_like(distances[0])
        for ant_index in range(num_ants):
            current_node = ant_positions[ant_index]
            unvisited_nodes = np.arange(len(distances))[np.logical_not(np.in1d(np.arange(len(distances)), ant_positions))]
            distances_to_unvisited_nodes = distances[current_node, unvisited_nodes]
            probabilities_to_unvisited_nodes = pheromone_matrix[current_node, unvisited_nodes] * (1.0 / distances_to_unvisited_nodes)
            probabilities_to_unvisited_nodes /= np.sum(probabilities_to_unvisited_nodes)
            probabilities[unvisited_nodes] += probabilities_to_unvisited_nodes

        # 让每只蚂蚁根据概率选择下一个节点
        next_nodes = np.zeros(num_ants, dtype=int)
        for ant_index in range(num_ants):
            next_nodes[ant_index] = np.random.choice(np.arange(len(distances)), p=probabilities)

        # 更新所有蚂蚁的路径长度和位置
        ant_path_lengths += distances[ant_positions, next_nodes]
        ant_positions = next_nodes

    # 计算所有蚂蚁的路径长度
    total_path_lengths = ant_path_lengths

    # 更新最佳路径和最佳路径长度
    best_path_index = np.argmin(total_path_lengths)
    if total_path_lengths[best_path_index] < best_path_length:
        best_path = ant_positions[best_path_index]
        best_path_length = total_path_lengths[best_path_index]

    # 更新信息素矩阵
    pheromone_matrix *= evaporation_rate
    for ant_index in range(num_ants):
        for i in range(len(distances) - 1):
            pheromone_matrix[ant_positions[i], ant_positions[i + 1]] += pheromone_factor / ant_path_lengths[ant_index]

# 输出最佳路径和最佳路径长度
print("Best path:", best_path)
print("Best path length:", best_path_length)