蚁群算法旅行商伪代码python

以下是蚁群算法解决旅行商问题的伪代码示例（使用Python编写）：

# 导入所需的库
import random

# 初始化参数
num_ants = 10  # 蚂蚁数量
num_iterations = 100  # 迭代次数
alpha = 1  # 信息素重要程度因子
beta = 2  # 启发式因子
rho = 0.5  # 信息素蒸发系数
Q = 100  # 信息素增加强度
num_cities = 10  # 城市数量

# 初始化城市距离矩阵
distance_matrix = [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
                   [1, 0, 2, 3, 4, 5, 6, 7, 8, 9],
                   [2, 2, 0, 3, 4, 5, 6, 7, 8, 9],
                   [3, 3, 3, 0, 4, 5, 6, 7, 8, 9],
                   [4, 4, 4, 4, 0, 5, 6, 7, 8, 9],
                   [5, 5, 5, 5, 5, 0, 6, 7, 8, 9],
                   [6, 6, 6, 6, 6, 6, 0, 7, 8, 9],
                   [7, 7, 7, 7, 7, 7, 7, 0, 8, 9],
                   [8, 8, 8, 8, 8, 8, 8, 8, 0, 9],
                   [9, 9, 9, 9, 9, 9, 9, 9, 9, 0]]

# 初始化信息素矩阵
pheromone_matrix = [[1 for _ in range(num_cities)] for _ in range(num_cities)]

# 初始化最佳路径和最短距离
best_path = []
best_distance = float('inf')

# 开始迭代
for iteration in range(num_iterations):
    # 初始化蚂蚁的位置和已访问城市列表
    ants = [[random.randint(0, num_cities-1)] for _ in range(num_ants)]
    visited = [[False for _ in range(num_cities)] for _ in range(num_ants)]
    
    # 计算每只蚂蚁的路径
    for ant in range(num_ants):
        for _ in range(num_cities-1):
            current_city = ants[ant][-1]
            unvisited_cities = [city for city in range(num_cities) if not visited[ant][city]]
            
            # 计算下一个城市的选择概率
            probabilities = []
            total = 0
            for city in unvisited_cities:
                pheromone = pheromone_matrix[current_city][city]
                distance = distance_matrix[current_city][city]
                probabilities.append((city, pheromone**alpha * (1/distance)**beta))
                total += pheromone**alpha * (1/distance)**beta
            
            # 根据选择概率选择下一个城市
            probabilities = [(city, prob/total) for city, prob in probabilities]
            next_city = random.choices([city for city, _ in probabilities], [prob for _, prob in probabilities])[0]
            
            # 更新蚂蚁的位置和已访问城市列表
            ants[ant].append(next_city)
            visited[ant][next_city] = True
    
    # 更新信息素矩阵
    delta_pheromone = [[0 for _ in range(num_cities)] for _ in range(num_cities)]
    for ant in range(num_ants):
        distance = sum(distance_matrix[ants[ant][i]][ants[ant][i+1]] for i in range(num_cities-1))
        if distance < best_distance:
            best_distance = distance
            best_path = ants[ant]
        for i in range(num_cities-1):
            delta_pheromone[ants[ant][i]][ants[ant][i+1]] += Q / distance
    
    pheromone_matrix = [[(1-rho)*pheromone + delta_pheromone[i][j] for j, pheromone in enumerate(row)] for i, row in enumerate(pheromone_matrix)]

# 输出最佳路径和最短距离
print("Best path:", best_path)
print("Best distance:", best_distance)