蚁群算法 路径规划 python

蚁群算法是一种模仿蚂蚁觅食行为的启发式优化算法，主要用于解决路径规划问题。它通过模拟蚂蚁在寻找食物时的行为，不断地在问题空间中搜索并更新路径，最终找到一条较优的解决方案。

在Python中，可以使用一些库来实现蚁群算法的路径规划。以下是一个简单的示例代码：

import numpy as np

class AntColony:
    def __init__(self, num_ants, num_iterations, num_cities, alpha=1, beta=5, rho=0.5, Q=100):
        self.num_ants = num_ants
        self.num_iterations = num_iterations
        self.num_cities = num_cities
        self.alpha = alpha
        self.beta = beta
        self.rho = rho
        self.Q = Q

        self.distances = np.array([[0, 2, 3, 0],
                                   [2, 0, 1, 4],
                                   [3, 1, 0, 2],
                                   [0, 4, 2, 0]])  # 城市之间的距离

        self.pheromone = np.ones((num_cities, num_cities))  # 信息素矩阵

    def run(self):
        best_path = None
        best_length = np.inf

        for i in range(self.num_iterations):
            all_paths = []
            all_lengths = []

            for ant in range(self.num_ants):
                path = self.construct_path()
                length = self.calculate_path_length(path)

                if length < best_length:
                    best_length = length
                    best_path = path

                all_paths.append(path)
                all_lengths.append(length)

            self.update_pheromone(all_paths, all_lengths)

        return best_path, best_length

    def construct_path(self):
        path = [0]
        visited = set([0])

        for _ in range(self.num_cities - 1):
            next_city = self.next_city(path[-1], visited)
            path.append(next_city)
            visited.add(next_city)

        return path

    def next_city(self, current_city, visited):
        unvisited = list(set(range(self.num_cities)) - visited)
        probabilities = []

        for city in unvisited:
            pheromone = self.pheromone[current_city][city]
            distance = self.distances[current_city][city]
            probability = pheromone**self.alpha / distance**self.beta
            probabilities.append(probability)

        probabilities = np.array(probabilities)
        probabilities = probabilities / probabilities.sum()

        next_city = np.random.choice(unvisited, p=probabilities)
        return next_city

    def calculate_path_length(self, path):
        length = 0

        for i in range(len(path) - 1):
            length += self.distances[path[i]][path[i + 1]]

        return length

    def update_pheromone(self, all_paths, all_lengths):
        self.pheromone *= (1 - self.rho)

        for i in range(len(all_paths)):
            path = all_paths[i]
            length = all_lengths[i]

            for j in range(len(path) - 1):
                city_a = path[j]
                city_b = path[j + 1]

                self.pheromone[city_a][city_b] += self.Q / length

if __name__ == '__main__':
    ant_colony = AntColony(num_ants=10, num_iterations=100, num_cities=4)
    best_path, best_length = ant_colony.run()
    print("Best path:", best_path)
    print("Best length:", best_length)

在上述代码中，我们使用了一个4个城市的路径规划问题作为示例，距离矩阵和信息素矩阵分别用`distances`和`pheromone`表示。你可以根据自己的实际问题进行修改和扩展。

希望对你有帮助！如果有任何疑问，请随时提问。