给我一个蚁群算法代码案例

以下是一个简单的蚁群算法代码示例，用于解决旅行商问题（TSP）：

import random

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

# 定义蚂蚁的数量和迭代次数
num_ants = 5
num_iterations = 10

# 定义蚁群算法的参数
alpha = 1.0
beta = 2.0
rho = 0.5

# 初始化信息素矩阵
pheromone_matrix = [[1.0 for j in range(len(distance_matrix))] for i in range(len(distance_matrix))]

# 定义计算下一个城市的函数
def select_next_city(current_city, available_cities):
    # 计算每个城市的概率
    probabilities = []
    total = 0.0
    for city in available_cities:
        pheromone = pheromone_matrix[current_city][city]
        distance = distance_matrix[current_city][city]
        probability = pow(pheromone, alpha) * pow(1.0 / distance, beta)
        probabilities.append((city, probability))
        total += probability

    # 根据概率选择下一个城市
    probabilities.sort(key=lambda x: x[1], reverse=True)
    roulette_wheel = random.uniform(0, total)
    wheel_position = 0.0
    for city, probability in probabilities:
        wheel_position += probability
        if wheel_position >= roulette_wheel:
            return city

# 定义蚂蚁类
class Ant:
    def __init__(self, start_city):
        self.visited_cities = [start_city]
        self.current_city = start_city
        self.total_distance = 0.0

    def move_to_next_city(self):
        available_cities = [i for i in range(len(distance_matrix)) if i not in self.visited_cities]
        next_city = select_next_city(self.current_city, available_cities)
        self.visited_cities.append(next_city)
        self.total_distance += distance_matrix[self.current_city][next_city]
        self.current_city = next_city

    def run(self):
        for i in range(len(distance_matrix) - 1):
            self.move_to_next_city()
        self.total_distance += distance_matrix[self.visited_cities[-1]][self.visited_cities[0]]

# 迭代蚁群算法
best_distance = float('inf')
best_solution = None
for iteration in range(num_iterations):
    solutions = []
    for ant in range(num_ants):
        start_city = random.randint(0, len(distance_matrix) - 1)
        ant = Ant(start_city)
        ant.run()
        solutions.append(ant)
        if ant.total_distance < best_distance:
            best_distance = ant.total_distance
            best_solution = ant.visited_cities

    # 更新信息素矩阵
    delta_pheromone_matrix = [[0.0 for j in range(len(distance_matrix))] for i in range(len(distance_matrix))]
    for solution in solutions:
        for i in range(len(solution.visited_cities) - 1):
            current_city = solution.visited_cities[i]
            next_city = solution.visited_cities[i + 1]
            delta_pheromone_matrix[current_city][next_city] += 1.0 / solution.total_distance
            delta_pheromone_matrix[next_city][current_city] += 1.0 / solution.total_distance
    for i in range(len(distance_matrix)):
        for j in range(len(distance_matrix)):
            pheromone_matrix[i][j] = (1.0 - rho) * pheromone_matrix[i][j] + delta_pheromone_matrix[i][j]

print(f"Best distance: {best_distance}")
print(f"Best solution: {best_solution}")

该代码中包含了以下步骤：

1. 定义TSP问题的距离矩阵。
2. 定义蚂蚁的数量和迭代次数。
3. 定义蚁群算法的参数。
4. 初始化信息素矩阵。
5. 定义计算下一个城市的函数。
6. 定义蚂蚁类，包含了选择下一个城市和计算总距离的方法。
7. 迭代蚁群算法，包括生成解、计算距离、更新信息素矩阵等步骤。
8. 输出最优解和最优距离。

这个示例代码可以解决一个小规模的TSP问题，你可以根据需要修改距离矩阵和参数来解决更大规模的问题。