帮我写一份蚁群算法路径规划的代码

好的，这里是一个简单的蚁群算法路径规划的 Python 代码，希望能帮助你：

import random

# 初始化参数
num_ants = 5  # 蚂蚁数量
num_cities = 10  # 城市数量
alpha = 1  # 信息素重要程度因子
beta = 2  # 启发式因子
rho = 0.5  # 信息素挥发因子
Q = 100  # 常量
max_iterations = 100  # 最大迭代次数

# 初始化城市位置和距离
positions = [(random.uniform(0, 100), random.uniform(0, 100)) for i in range(num_cities)]
distances = [[0 for j in range(num_cities)] for i in range(num_cities)]
for i in range(num_cities):
    for j in range(num_cities):
        if i != j:
            distances[i][j] = ((positions[i][0] - positions[j][0]) ** 2 + (positions[i][1] - positions[j][1]) ** 2) ** 0.5

# 初始化信息素和启发因子
pheromones = [[1 for j in range(num_cities)] for i in range(num_cities)]
heuristics = [[1 / distances[i][j] for j in range(num_cities)] for i in range(num_cities)]

# 开始迭代
best_path = None
best_distance = float('inf')
for iteration in range(max_iterations):
    # 初始化蚂蚁位置和路径
    ants = [[i, random.randint(0, num_cities - 1)] for i in range(num_ants)]
    paths = [[i] for i in range(num_ants)]

    # 蚂蚁开始寻找路径
    for step in range(num_cities - 1):
        for i in range(num_ants):
            cur_city = ants[i][-1]
            # 计算下一个城市的选择概率
            prob = [pheromones[cur_city][j] ** alpha * heuristics[cur_city][j] ** beta for j in range(num_cities)]
            prob_sum = sum(prob)
            prob = [p / prob_sum for p in prob]
            # 选择下一个城市
            next_city = random.choices(range(num_cities), prob)[0]
            # 更新路径和蚂蚁位置
            paths[i].append(next_city)
            ants[i].append(next_city)

    # 计算路径长度和更新信息素
    for i in range(num_ants):
        distance = 0
        for j in range(num_cities):
            distance += distances[paths[i][j - 1]][paths[i][j]]
        # 更新最优路径
        if distance < best_distance:
            best_path = paths[i]
            best_distance = distance
        # 更新信息素
        for j in range(num_cities):
            pheromones[paths[i][j - 1]][paths[i][j]] *= (1 - rho)
            pheromones[paths[i][j - 1]][paths[i][j]] += rho * Q / distance

# 输出最优路径和长度
print('Best path:', best_path)
print('Best distance:', best_distance)

这个代码实现了一个简单的蚁群算法路径规划，包括初始化城市位置和距离、信息素和启发因子，以及迭代过程中蚂蚁的移动、路径长度的计算和信息素的更新。你可以根据自己的需求进行修改和优化。