人工蜂群算法求解TSP问题，给出代码

以下是一个简单的Python实现：

import random

# 计算两点之间的距离
def distance(city1, city2):
    x1, y1 = city1
    x2, y2 = city2
    return ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5

# 创建TSP问题的初始城市列表
def create_cities(n):
    cities = []
    for i in range(n):
        x = random.random()
        y = random.random()
        cities.append((x, y))
    return cities

# 计算一条路径的总距离
def path_distance(path, cities):
    distance = 0
    for i in range(len(path)):
        j = (i + 1) % len(path)
        city1 = cities[path[i]]
        city2 = cities[path[j]]
        distance += distance(city1, city2)
    return distance

# 初始化一群蜜蜂
def create_bees(num_bees, num_sites):
    bees = []
    for i in range(num_bees):
        site = random.randint(0, num_sites - 1)
        bees.append((site, path_distance([site], cities)))
    return bees

# 找到最优路径
def optimize(num_epochs, num_bees, num_sites, elite_sites, patch_size):
    cities = create_cities(num_sites)
    bees = create_bees(num_bees, num_sites)
    best_path = None
    best_distance = None
    for epoch in range(num_epochs):
        # 计算每个位置的相对重要性
        fitnesses = [1.0 / bee[1] for bee in bees]
        total_fitness = sum(fitnesses)
        probs = [fitness / total_fitness for fitness in fitnesses]
        # 选择精英位置
        elite_indices = sorted(range(num_bees), key=lambda i: bees[i][1])[:elite_sites]
        elite_sites = [bees[i][0] for i in elite_indices]
        # 在精英位置周围搜索新位置
        for site in elite_sites:
            for i in range(patch_size):
                new_site = (site + i) % num_sites
                new_path = [new_site]
                for j in range(num_sites - 1):
                    # 选择随机蜜蜂
                    probs_cumulative = [sum(probs[:k+1]) for k in range(num_bees)]
                    random_num = random.random()
                    bee_index = next(i for i, prob in enumerate(probs_cumulative) if prob >= random_num)
                    bee_path = bees[bee_index][0]
                    new_path.append(bee_path)
                new_distance = path_distance(new_path, cities)
                # 更新位置和距离
                if new_distance < bees[site][1]:
                    bees[site] = (new_site, new_distance)
        # 找到最优路径
        for bee in bees:
            if best_distance is None or bee[1] < best_distance:
                best_path = [bee[0]]
                best_distance = bee[1]
    return best_path, best_distance

使用示例：

num_epochs = 100
num_bees = 10
num_sites = 20
elite_sites = 3
patch_size = 5
best_path, best_distance = optimize(num_epochs, num_bees, num_sites, elite_sites, patch_size)
print("Best path:", best_path)
print("Best distance:", best_distance)