蚁群算法路线规划代码

蚁群算法（Ant Colony Optimization, ACO）是一种模拟蚂蚁寻找食物路径的优化搜索方法，常用于解决复杂的路线规划问题。以下是一个简化的蚁群算法路线规划的代码概述：

# 导入所需库
import numpy as np
import random

# 定义蚂蚁类
class Ant:
    def __init__(self, colony, graph):
        self.colony = colony
        self.graph = graph
        self.position = None
        self.best_path = []
        self pheromone = 0
        self.path_cost = float('inf')

    # 随机起始位置
    def initialize(self):
        self.position = random.choice(list(graph.nodes))

    # 进行搜索
    def search(self):
        while not self.colony.stop:
            # 选择下一个节点
            next_node = self.select_next_node()
            # 更新路径和状态
            self.update_path(next_node)
        return self.best_path

    # 选择节点的函数，考虑了pheromone和距离
    def select_next_node(self):
        neighbors = self.graph.neighbors(self.position)
        probabilities = [self.pheromone[node] / (self.pheromone[node] + graph[node].weight) for node in neighbors]
        total_prob = sum(probabilities)
        probabilities = [prob / total_prob for prob in probabilities]
        return random.choices(neighbors, probabilities)

    # 更新路径和状态
    def update_path(self, next_node):
        if next_node not in self.best_path or self.graph.get_weight(self.position, next_node) < self.path_cost:
            self.path_cost = self.graph.get_weight(self.position, next_node)
            self.best_path.append(next_node)
            self.position = next_node
            self.colony.update_pheromone(self.position)

# 定义蚁群类
class AntColony:
    def __init__(self, graph, ants_count, iterations):
        self.graph = graph
        self.ants = [Ant(self, graph) for _ in range(ants_count)]
        self.iterations = iterations
        self.pheromone_matrix = np.ones((graph.nodes_count, graph.nodes_count))  # 初始化pheromone矩阵
        self.stop = False

    # 更新pheromone值
    def update_pheromone(self, node):
        # 更新节点间的pheromone
        # 实现自适应更新规则
        ...

    # 主循环
    def run(self):
        for _ in range(self.iterations):
            for ant in self.ants:
                ant.initialize()
                ant.search()
                self.update_pheromone(ant.position)
        return self.get_best_path()

# 示例使用
if __name__ == "__main__":
    graph = create_graph()  # 创建图数据结构
    ant_colony = AntColony(graph, ants_count=100, iterations=1000)
    best_path = ant_colony.run()
    print("Best found path:", best_path)