图解蚁群算法python

蚁群算法是一种模拟蚂蚁觅食行为的启发式算法，用于解决优化问题。下面是一个简单的Python代码示例，用于图解蚁群算法：

import numpy as np

# 创建地图
graph = [[0, 2, 4, 5],
         [2, 0, 1, 6],
         [4, 1, 0, 3],
         [5, 6, 3, 0]]

# 蚂蚁数量
num_ants = 10

# 蚂蚁位置
ant_location = np.zeros(num_ants, dtype=np.int)

# 蚂蚁经过的路径
ant_path = np.zeros((num_ants, len(graph)), dtype=np.int)

# 蚂蚁访问状态
visited = np.zeros(len(graph), dtype=bool)

# 蚂蚁已经访问的城市数量
visited_count = np.zeros(num_ants, dtype=np.int)

# 蚂蚁到达终点的标志
finished = np.zeros(num_ants, dtype=bool)

# 启发因子（信息素浓度和距离的权重）
alpha = 1.0
beta = 2.0

# 信息素浓度
pheromone = np.ones_like(graph)

# 距离矩阵
distance = np.array(graph)

# 迭代次数
num_iterations = 100

# 主循环
for iteration in range(num_iterations):
    # 每只蚂蚁选择下一个城市
    for ant in range(num_ants):
        if not finished[ant]:
            current_city = ant_location[ant]
            unvisited_cities = np.where(~visited)

            # 计算城市之间的转移概率
            probabilities = np.power(pheromone[current_city, unvisited_cities], alpha) * np.power(1.0 / distance[current_city, unvisited_cities], beta)
            probabilities /= np.sum(probabilities)

            # 选择下一个城市
            next_city = np.random.choice(unvisited_cities, p=probabilities)
            ant_path[ant, visited_count[ant]] = next_city
            visited[next_city] = True
            visited_count[ant] += 1
            ant_location[ant] = next_city

            # 判断是否到达终点
            if visited_count[ant] == len(graph) - 1:
                finished[ant] = True

    # 更新信息素浓度
    pheromone *= 0.5  # 挥发
    for ant in range(num_ants):
        for i in range(len(graph) - 1):
            city_1 = ant_path[ant, i]
            city_2 = ant_path[ant, i + 1]
            pheromone[city_1, city_2] += 1.0 / distance[city_1, city_2]

# 打印结果
for ant in range(num_ants):
    print("Ant {}: {}".format(ant, ant_path[ant]))

这段代码实现了蚁群算法的基本过程，包括蚂蚁选择下一个城市、更新信息素浓度等步骤。在每次迭代中，蚂蚁根据信息素浓度和距离的权重选择下一个城市，直到所有蚂蚁都到达终点或达到最大迭代次数为止。最终打印出每只蚂蚁的路径。你可以根据自己的优化问题进行相应的修改和扩展。