蚁群算法优化的代码

下面是一个简单的蚁群算法优化的 Python 代码：

import random

def ant_colony_optimization(graph, start_node, end_node, ant_count, alpha, beta, evaporation_rate, Q):
    # 初始化信息素矩阵
    pheromone = [[1 / (len(graph) * len(graph[0])) for _ in range(len(graph[0]))] for _ in range(len(graph))]
    # 初始化最优解
    best_path = []
    best_distance = float('inf')
    # 迭代优化
    for _ in range(100):
        # 每只蚂蚁都从起点出发
        paths = [[start_node] for _ in range(ant_count)]
        # 每只蚂蚁都随机选择下一步的节点
        for i in range(len(paths[0]) - 1, len(graph)):
            for j in range(len(paths)):
                node = paths[j][-1]
                # 计算节点的选择概率
                probabilities = []
                for k in range(len(graph[node])):
                    if k not in paths[j]:
                        probabilities.append(pheromone[node][k] ** alpha * (1 / graph[node][k]) ** beta)
                    else:
                        probabilities.append(0)
                # 根据选择概率选择下一步的节点
                if sum(probabilities) > 0:
                    probabilities = [p / sum(probabilities) for p in probabilities]
                    next_node = random.choices(range(len(graph)), weights=probabilities)[0]
                    paths[j].append(next_node)
        # 计算每条路径的距离
        distances = [sum([graph[paths[j][k]][paths[j][k + 1]] for k in range(len(paths[j]) - 1)]) for j in range(len(paths))]
        # 更新最优解
        if min(distances) < best_distance:
            best_distance = min(distances)
            best_path = paths[distances.index(best_distance)]
        # 更新信息素矩阵
        for i in range(len(pheromone)):
            for j in range(len(pheromone[i])):
                pheromone[i][j] *= (1 - evaporation_rate)
                for k in range(len(paths)):
                    if j in paths[k] and i in paths[k][paths[k].index(j) + 1:]:
                        pheromone[i][j] += Q / distances[k]
    return best_path, best_distance

其中，graph 表示节点之间的距离矩阵，start_node 表示起点，end_node 表示终点，ant_count 表示蚂蚁数量，alpha 和 beta 分别表示信息素和距离的重要程度，evaporation_rate 表示信息素挥发率，Q 表示信息素增加的量。函数返回最优解的路径和距离。