写一个人工鱼群算法解决TSP问题的PYTHON代码

以下是一个简单的人工鱼群算法解决TSP问题的PYTHON代码：

import random
import numpy as np

# 定义TSP问题中的城市数量
city_num = 20

# 定义人工鱼群算法的参数
alpha = 0.6  # 个体运动速度
beta = 0.2  # 鱼群聚集速度
delta = 0.8  # 拥挤度
visual = 0.15  # 可视范围
step = 0.2  # 步长
iter_num = 100  # 迭代次数
fish_num = 50  # 鱼群数量
best_distance = np.inf  # 最优解的路径长度

# 生成城市坐标
city_pos = np.random.rand(city_num, 2)

# 计算城市之间的距离
city_distance = np.zeros((city_num, city_num))
for i in range(city_num):
    for j in range(city_num):
        if i != j:
            city_distance[i, j] = np.linalg.norm(city_pos[i, :] - city_pos[j, :])

# 随机初始化鱼群位置
fish_pos = np.zeros((fish_num, city_num), dtype=int)
for i in range(fish_num):
    fish_pos[i, :] = np.random.permutation(city_num)

# 开始迭代
for k in range(iter_num):
    # 计算每条路径的适应度
    fitness = np.zeros(fish_num)
    for i in range(fish_num):
        distance = 0
        for j in range(city_num - 1):
            distance += city_distance[fish_pos[i, j], fish_pos[i, j + 1]]
        distance += city_distance[fish_pos[i, -1], fish_pos[i, 0]]
        fitness[i] = 1 / distance
        if distance < best_distance:
            best_distance = distance
            best_path = fish_pos[i, :]

    # 计算每条路径的相对适应度
    relative_fitness = fitness / np.sum(fitness)

    # 计算每条路径的移动距离和方向
    move_distance = np.zeros((fish_num, city_num))
    move_direction = np.zeros((fish_num, city_num))
    for i in range(fish_num):
        for j in range(city_num):
            temp_pos = np.copy(fish_pos[i, :])
            temp_pos = np.roll(temp_pos, -j)
            temp_pos = temp_pos[1:]
            visible_fish_num = 0
            for l in range(fish_num):
                if l != i and np.linalg.norm(temp_pos - fish_pos[l, :-1]) < visual:
                    visible_fish_num += 1
                    move_distance[i, j] += relative_fitness[l]
                    move_direction[i, j] += relative_fitness[l] * (fish_pos[l, j] - fish_pos[i, j])
            if visible_fish_num > 0:
                move_direction[i, j] /= visible_fish_num

    # 计算每条路径的速度和移动位置
    fish_speed = np.zeros((fish_num, city_num))
    fish_pos_new = np.zeros((fish_num, city_num), dtype=int)
    for i in range(fish_num):
        for j in range(city_num):
            fish_speed[i, j] = alpha * fish_speed[i, j] + beta * move_distance[i, j] + delta * move_direction[i, j]
            fish_pos_new[i, j] = np.mod(fish_pos[i, j] + np.round(step * fish_speed[i, j]), city_num)

    fish_pos = fish_pos_new

print("最优路径长度：", best_distance)
print("最优路径：", best_path)

在这个代码中，我们首先初始化了TSP问题中的城市坐标，然后随机初始化了鱼群的位置。接着，我们开始迭代，计算每条路径的适应度和相对适应度，然后计算每条路径的移动距离和方向，最后计算每条路径的速度和移动位置，更新鱼群位置。最终输出最优路径和路径长度。

需要注意的是，这个代码只是一个简单的实现，可能无法得到最优解。如果需要得到更好的解决方案，可以调整参数或使用其他优化算法。