写一个混合蛙跳算法解决TSP问题的PYTHON代码

好的，以下是一个简单的混合蛙跳算法解决TSP问题的PYTHON代码：

import random
import numpy as np
import math

# TSP问题的城市数量
N = 10

# 每只青蛙的初始位置
init_pos = np.zeros((N, 2))
for i in range(N):
    init_pos[i][0] = random.uniform(-5, 5)
    init_pos[i][1] = random.uniform(-5, 5)

# 计算距离矩阵
dist_matrix = np.zeros((N, N))
for i in range(N):
    for j in range(N):
        dist_matrix[i][j] = math.sqrt(pow(init_pos[i][0]-init_pos[j][0], 2) + pow(init_pos[i][1]-init_pos[j][1], 2))

# 距离矩阵中所有元素加上一个极大值，以便计算概率
dist_matrix += np.max(dist_matrix)

# 距离矩阵的倒数，用于计算概率
dist_matrix_inv = 1 / dist_matrix

# 青蛙的个数
num_frogs = 20

# 最大迭代次数
max_iter = 100

# 每次迭代中保留的最优解的个数
num_keep = 5

# 跳跃步长
step_size = 0.5

# 每个青蛙的初始路径
init_path = np.arange(N)
for i in range(num_frogs):
    np.random.shuffle(init_path)
    init_path = init_path.copy()
    
# 计算路径长度
def get_path_length(path):
    length = 0
    for i in range(N-1):
        length += dist_matrix[path[i]][path[i+1]]
    length += dist_matrix[path[N-1]][path[0]]
    return length

# 计算每个路径的适应度
def get_fitness(path):
    length = get_path_length(path)
    return 1 / length

# 对路径进行变异
def mutate(path):
    new_path = path.copy()
    i = random.randint(0, N-1)
    j = random.randint(0, N-1)
    new_path[i], new_path[j] = new_path[j], new_path[i]
    return new_path

# 执行混合蛙跳算法
best_path = init_path.copy()
best_fitness = get_fitness(best_path)

for iter in range(max_iter):
    # 随机选择一只青蛙作为“王子”
    prince = random.randint(0, num_frogs-1)
    
    # 对“王子”进行变异
    prince_path = mutate(init_path[prince])
    
    # 计算“王子”的适应度
    prince_fitness = get_fitness(prince_path)
    
    # 随机选择一只青蛙作为“公主”
    princess = random.randint(0, num_frogs-1)
    
    # 对“公主”进行变异
    princess_path = mutate(init_path[princess])
    
    # 计算“公主”的适应度
    princess_fitness = get_fitness(princess_path)
    
    # 计算每只青蛙的跳跃概率
    prob = np.zeros(num_frogs)
    for i in range(num_frogs):
        if i == prince or i == princess:
            prob[i] = 0
        else:
            prob[i] = (dist_matrix_inv[init_path[i-1]][init_path[i]] + dist_matrix_inv[init_path[i]][init_path[i+1]]) / (2 * (N-1))
    
    # 对青蛙进行跳跃
    for i in range(num_frogs):
        if i == prince:
            # “王子”跳到“公主”所在的位置
            init_path[i] = princess_path
            continue
        if i == princess:
            # “公主”跳到“王子”所在的位置
            init_path[i] = prince_path
            continue
        # 计算跳跃步长
        step = step_size * (2 * random.random() - 1)
        # 随机选择一个青蛙作为“导师”
        teacher = random.randint(0, num_frogs-1)
        if teacher == i:
            # 如果“导师”是自己，则直接变异
            new_path = mutate(init_path[i])
        else:
            # 否则以“导师”的路径为基础，进行差分变异
            new_path = init_path[teacher].copy()
            j = random.randint(0, N-1)
            new_path[j] = init_path[teacher][j] + step * (init_path[i][j] - init_path[teacher][j])
        # 计算新路径的适应度
        new_fitness = get_fitness(new_path)
        # 根据概率决定是否接受新路径
        if new_fitness > prince_fitness and random.random() < prob[i]:
            init_path[i] = new_path
        elif new_fitness > princess_fitness and random.random() < prob[i]:
            init_path[i] = new_path
            
    # 保留每次迭代中的最优解
    fitness_list = [get_fitness(init_path[i]) for i in range(num_frogs)]
    best_index = np.argsort(fitness_list)[::-1][:num_keep]
    for i in range(num_keep):
        if fitness_list[best_index[i]] > best_fitness:
            best_path = init_path[best_index[i]].copy()
            best_fitness = fitness_list[best_index[i]]

# 输出最优解
print("Best Path: ", best_path)
print("Best Fitness: ", best_fitness)