给我一个人工蜂群算法解决tsp代码

以下是一个简单的Python代码实现，使用人工蜂群算法解决TSP问题：

import numpy as np
import random
import math

# 初始化参数
num_city = 20   # 城市个数
num_bee = 20    # 蜜蜂个数
max_iter = 100  # 最大迭代次数
limit = 10      # 每只蜜蜂最大搜索次数

# 初始化城市坐标
city_coords = np.random.rand(num_city, 2) * 100

# 计算城市之间的距离矩阵
def calc_distance_matrix(coords):
    dist_matrix = np.zeros((num_city, num_city))
    for i in range(num_city):
        for j in range(num_city):
            dist_matrix[i][j] = math.sqrt((coords[i][0]-coords[j][0])**2 + (coords[i][1]-coords[j][1])**2)
    return dist_matrix

dist_matrix = calc_distance_matrix(city_coords)

# 初始化蜜蜂群体
class Bee:
    def __init__(self):
        self.path = np.random.permutation(num_city)      # 随机初始化路径
        self.dist = self.calc_path_distance()            # 计算路径距离
        self.counter = 0                                # 搜索次数计数器
    
    # 计算路径距离
    def calc_path_distance(self):
        total_dist = 0
        for i in range(num_city-1):
            total_dist += dist_matrix[self.path[i]][self.path[i+1]]
        total_dist += dist_matrix[self.path[num_city-1]][self.path[0]]
        return total_dist
    
    # 搜索新的路径
    def search_new_path(self):
        while True:
            new_path = self.path.copy()
            i, j = np.random.choice(num_city, 2, replace=False)
            new_path[i], new_path[j] = new_path[j], new_path[i]
            new_dist = self.calc_path_distance()
            if new_dist < self.dist:
                self.path = new_path
                self.dist = new_dist
                self.counter = 0
                break
            else:
                self.counter += 1
                if self.counter >= limit:
                    break

bees = [Bee() for i in range(num_bee)]

# 迭代搜索
for iter in range(max_iter):
    for bee in bees:
        bee.search_new_path()
    
    # 根据路径距离排序，选择最优蜜蜂
    bees.sort(key=lambda bee: bee.dist)
    best_bee = bees[0]
    
    # 生成新的蜜蜂群体
    new_bees = []
    for i in range(num_bee):
        new_bee = Bee()
        j = random.randint(0, int(num_bee/2))
        new_bee.path = bees[j].path.copy()
        new_bee.search_new_path()
        new_bees.append(new_bee)
    bees = new_bees
    
    print("Iteration %d, Best Distance: %.2f" % (iter+1, best_bee.dist))
 
# 输出最优路径
print("Best Path: ", best_bee.path)

这段代码中，首先初始化城市坐标和距离矩阵。然后定义了一个Bee类，表示一只蜜蜂，包括当前路径、路径长度和搜索次数计数器等属性，以及计算路径长度和搜索新路径的方法。接着初始化蜜蜂群体，包括多只蜜蜂。在每次迭代中，每只蜜蜂搜索新的路径，更新自己的路径和路径长度，并根据路径长度排序选择最优蜜蜂。然后生成新的蜜蜂群体，包括多只新的蜜蜂，其中一部分蜜蜂的路径参考当前最优蜜蜂的路径。最后输出最优路径。