蚁群算法_Python实现

import numpy as np

class AntColony:
    def __init__(self, distances, n_ants, n_best, n_iterations, decay, alpha=1, beta=1):
        # 初始化蚁群算法的参数
        self.distances = distances  # 城市间的距离矩阵
        # 初始化信息素矩阵, 行列为输入的distances的行列, 每个信息素的初始值为1/distances的行数, 也就是说每行信息素的和都=1
        self.pheromone = np.ones(self.distances.shape) / len(distances)
        self.all_inds = range(len(distances))  # 城市索引, 列表=[0,1,2,3...len(distances)]
        self.n_ants = n_ants  # 蚂蚁数量
        self.n_best = n_best  # 选择的最佳路径数量, b_best=1
        self.n_iterations = n_iterations  # 迭代次数
        self.decay = decay  # 信息素衰减率
        self.alpha = alpha  # 信息素重要性因子
        self.beta = beta  # 启发式信息重要性因子

    def run(self):
        shortest_path = None  # 当前迭代中的最短路径
        all_time_shortest_path = ("placeholder", np.inf)  # 全局最短路径, np.inf代表无穷大
        for i in range(self.n_iterations):
            all_paths = self.gen_all_paths()  # 生成所有蚂蚁的路径
            self.spread_pheronome(al