写一个用自适应蚁群算法计算最优解的旅行商问题

好的，以下是一个使用自适应蚁群算法计算最优解的旅行商问题的示例代码：

import numpy as np

# 定义问题参数
n_cities = 20  # 城市数量
n_ants = 20  # 蚂蚁数量
alpha = 1.0  # 信息素重要程度因子
beta = 5.0  # 启发函数重要程度因子
rho = 0.1  # 信息素挥发因子
Q = 100  # 常数因子
t0 = 1  # 初始信息素浓度
max_iter = 100  # 最大迭代次数

# 生成随机城市坐标
np.random.seed(42)
cities = np.random.rand(n_cities, 2)

# 计算距离矩阵
dist_mat = np.zeros((n_cities, n_cities))
for i in range(n_cities):
    for j in range(n_cities):
        dist_mat[i, j] = np.sqrt((cities[i, 0] - cities[j, 0]) ** 2 
                                 + (cities[i, 1] - cities[j, 1]) ** 2)

# 初始化信息素浓度矩阵
pher_mat = np.ones((n_cities, n_cities)) * t0

# 定义启发函数
def heuristic_func(dist):
    return 1.0 / dist

# 定义选择下一个城市的策略
def select_next_city(cur_city, unvisited_cities, pher_mat, dist_mat, alpha, beta):
    # 计算下一个城市的概率分布
    probs = np.zeros(len(unvisited_cities))
    for i, city in enumerate(unvisited_cities):
        probs[i] = (pher_mat[cur_city, city] ** alpha) * (heuristic_func(dist_mat[cur_city, city]) ** beta)
    probs /= np.sum(probs)
    # 根据概率分布选择下一个城市
    next_city = np.random.choice(unvisited_cities, p=probs)
    return next_city

# 定义更新信息素浓度的策略
def update_pheromone(trails, pher_mat, rho, Q):
    delta_pher_mat = np.zeros((n_cities, n_cities))
    for trail in trails:
        for i in range(n_cities - 1):
            delta_pher_mat[trail[i], trail[i+1]] += Q / dist_mat[trail[i], trail[i+1]]
        delta_pher_mat[trail[-1], trail[0]] += Q / dist_mat[trail[-1], trail[0]]
    pher_mat *= (1 - rho)
    pher_mat += delta_pher_mat

# 定义自适应参数更新策略
def update_parameters(success_count, total_count, alpha, beta):
    # 计算成功率
    success_rate = success_count / total_count
    # 根据成功率动态调整alpha和beta
    if success_rate > 0.95:
        alpha *= 1.05
        beta *= 1.05
    elif success_rate < 0.05:
        alpha *= 0.95
        beta *= 0.95
    return alpha, beta

# 迭代搜索
best_dist = np.inf
best_path = None
for iter in range(max_iter):
    # 初始化蚂蚁位置和已访问城市
    ant_positions = np.random.randint(0, n_cities, size=n_ants)
    visited_cities = np.zeros((n_ants, n_cities), dtype=bool)
    visited_cities[np.arange(n_ants), ant_positions] = True
    # 搜索路径
    for i in range(n_cities - 1):
        # 选择下一个城市
        next_cities = []
        for j in range(n_ants):
            unvisited_cities = np.where(~visited_cities[j])[0]
            next_city = select_next_city(ant_positions[j], unvisited_cities, pher_mat, dist_mat, alpha, beta)
            ant_positions[j] = next_city
            visited_cities[j, next_city] = True
            next_cities.append(next_city)
        # 更新信息素浓度
        update_pheromone(next_cities, pher_mat, rho, Q)
    # 计算本次迭代的最优解
    dists = []
    paths = []
    for j in range(n_ants):
        path = []
        dist = 0
        cur_city = ant_positions[j]
        unvisited_cities = np.where(~visited_cities[j])[0]
        while len(unvisited_cities) > 0:
            next_city = select_next_city(cur_city, unvisited_cities, pher_mat, dist_mat, alpha, beta)
            path.append(cur_city)
            dist += dist_mat[cur_city, next_city]
            cur_city = next_city
            unvisited_cities = np.where(~visited_cities[j])[0]
        path.append(cur_city)
        dist += dist_mat[cur_city, ant_positions[j]]
        dists.append(dist)
        paths.append(path)
    best_idx = np.argmin(dists)
    if dists[best_idx] < best_dist:
        best_dist = dists[best_idx]
        best_path = paths[best_idx]
    # 更新自适应参数
    alpha, beta = update_parameters(len(paths), n_ants, alpha, beta)
    # 打印迭代信息
    print(f"iter {iter}: best_dist={best_dist}")

在上述代码中，我们使用了一个自适应参数更新策略，根据每次迭代的成功率来动态调整alpha和beta的大小。在每次迭代结束后，我们根据所有蚂蚁的路径更新信息素浓度矩阵，并计算最优路径。最后，我们打印出每次迭代的最优解。