给出蜂群算法解决Tsp问题的实例代码

以下是一个使用蜂群算法解决TSP问题的Python代码示例：

import numpy as np
import random

# 定义TSP问题的距离矩阵
distance_matrix = np.array([
    [0, 10, 15, 20],
    [10, 0, 35, 25],
    [15, 35, 0, 30],
    [20, 25, 30, 0]
])

# 蚂蚁类
class Ant:
    def __init__(self, start_city, alpha, beta, pheromone_matrix):
        self.start_city = start_city # 起始城市
        self.alpha = alpha # alpha参数
        self.beta = beta # beta参数
        self.pheromone_matrix = pheromone_matrix # 信息素矩阵
        self.visited = [start_city] # 已访问城市列表
        self.unvisited = set(range(len(distance_matrix))) - set([start_city]) # 未访问城市列表

    # 计算从当前城市到所有未访问城市的转移概率
    def compute_probabilities(self):
        probabilities = []
        total = 0
        for city in self.unvisited:
            pheromone = self.pheromone_matrix[self.visited[-1]][city]
            distance = distance_matrix[self.visited[-1]][city]
            probability = pow(pheromone, self.alpha) * pow(1.0/distance, self.beta)
            probabilities.append((city, probability))
            total += probability
        # 归一化
        probabilities = [(city, probability/total) for (city, probability) in probabilities]
        return probabilities

    # 选择下一个城市
    def choose_next_city(self):
        probabilities = self.compute_probabilities()
        r = random.uniform(0, 1)
        total = 0
        for (city, probability) in probabilities:
            total += probability
            if total >= r:
                return city

    # 蚂蚁走一步
    def move(self):
        next_city = self.choose_next_city()
        self.visited.append(next_city)
        self.unvisited.remove(next_city)

    # 计算路径长度
    def path_length(self):
        length = 0
        for i in range(len(self.visited)-1):
            length += distance_matrix[self.visited[i]][self.visited[i+1]]
        length += distance_matrix[self.visited[-1]][self.visited[0]]
        return length

# 蜂群算法类
class BeeColony:
    def __init__(self, num_ants, num_iterations, alpha, beta, rho, q):
        self.num_ants = num_ants # 蚂蚁数量
        self.num_iterations = num_iterations # 迭代次数
        self.alpha = alpha # alpha参数
        self.beta = beta # beta参数
        self.rho = rho # 信息素挥发因子
        self.q = q # 信息素增加强度
        self.pheromone_matrix = np.ones((len(distance_matrix), len(distance_matrix))) # 信息素矩阵
        self.best_path = None # 最佳路径
        self.best_path_length = np.inf # 最佳路径长度

    # 运行蜂群算法
    def run(self):
        for iteration in range(self.num_iterations):
            # 初始化蚂蚁
            ants = [Ant(i, self.alpha, self.beta, self.pheromone_matrix) for i in range(self.num_ants)]
            # 蚂蚁走一步
            for ant in ants:
                while ant.unvisited:
                    ant.move()
                # 更新最佳路径
                if ant.path_length() < self.best_path_length:
                    self.best_path = ant.visited
                    self.best_path_length = ant.path_length()
            # 更新信息素
            self.update_pheromone(ants)
            # 输出当前迭代的结果
            print("Iteration", iteration+1, "Best Path", self.best_path, "Best Path Length", self.best_path_length)

    # 更新信息素
    def update_pheromone(self, ants):
        # 信息素挥发
        self.pheromone_matrix = (1-self.rho) * self.pheromone_matrix
        # 信息素增加
        for ant in ants:
            path_length = ant.path_length()
            for i in range(len(ant.visited)-1):
                city1 = ant.visited[i]
                city2 = ant.visited[i+1]
                self.pheromone_matrix[city1][city2] += self.q/path_length
                self.pheromone_matrix[city2][city1] += self.q/path_length

# 运行蜂群算法
bee_colony = BeeColony(num_ants=10, num_iterations=100, alpha=1, beta=5, rho=0.5, q=100)
bee_colony.run()