蚁群算法轮盘赌python代码

蚁群算法是一种模拟蚂蚁觅食行为的优化算法，常用于解决组合优化问题。而轮盘赌是蚁群算法中的一种选择策略，用于确定下一步的行动。

以下是一个使用轮盘赌选择策略的蚁群算法的Python代码示例：

import random

# 初始化蚂蚁数量、城市数量和信息素矩阵
ant_num = 10
city_num = 5
pheromone = [[1.0 for _ in range(city_num)] for _ in range(city_num)]

# 初始化城市距离矩阵
distance = [
    [0, 2, 3, 4, 5],
    [2, 0, 6, 7, 8],
    [3, 6, 0, 9, 10],
    [4, 7, 9, 0, 11],
    [5, 8, 10, 11, 0]
]

# 轮盘赌选择下一个城市
def roulette_selection(probabilities):
    total = sum(probabilities)
    r = random.uniform(0, total)
    accumulate = 0
    for i in range(len(probabilities)):
        accumulate += probabilities[i]
        if accumulate >= r:
            return i

# 蚁群算法主循环
for _ in range(100):
    # 初始化每只蚂蚁的当前城市和已访问城市列表
    ants = [[random.randint(0, city_num-1)] for _ in range(ant_num)]
    visited = [[False for _ in range(city_num)] for _ in range(ant_num)]

    # 每只蚂蚁按照轮盘赌选择下一个城市
    for _ in range(city_num-1):
        for ant in ants:
            current_city = ant[-1]
            unvisited_cities = [i for i in range(city_num) if not visited[ant_num][i]]
            probabilities = [pheromone[current_city][city] / distance[current_city][city] for city in unvisited_cities]
            next_city = roulette_selection(probabilities)
            ant.append(next_city)
            visited[ant_num][next_city] = True

    # 更新信息素矩阵
    delta_pheromone = [[0.0 for _ in range(city_num)] for _ in range(city_num)]
    for ant in ants:
        for i in range(city_num-1):
            current_city = ant[i]
            next_city = ant[i+1]
            delta_pheromone[current_city][next_city] += 1 / distance[current_city][next_city]
    for i in range(city_num):
        for j in range(city_num):
            pheromone[i][j] = (1 - 0.5) * pheromone[i][j] + delta_pheromone[i][j]

# 输出最优路径
best_ant = max(ants, key=lambda x: sum(distance[x[i]][x[i+1]] for i in range(city_num-1)))
best_path = [str(city) for city in best_ant]
print("Best path:", "->".join(best_path))

这段代码实现了一个简单的蚁群算法，其中使用了轮盘赌选择策略来确定下一个城市。代码中的城市数量为5，距离矩阵和信息素矩阵都是手动设置的示例数据。你可以根据实际问题进行相应的修改。