Python实现Ant Colony Optimization（蚁群优化）算法示例及解析

蚁群优化算法模拟了蚂蚁寻找食物的行为，通过模拟蚂蚁在解空间中的搜索过程，寻找最优解。这种算法可用于解决诸如路径规划、组合优化等问题。

import random

# 初始化蚁群
class AntColony:
    def __init__(self, num_ants, num_nodes):
        self.num_ants = num_ants
        self.num_nodes = num_nodes
        self.pheromone = [[1 for _ in range(num_nodes)] for _ in range(num_nodes)]

    def update_pheromone(self, best_solution):
        evaporation_rate = 0.5
        for i in range(self.num_nodes):
            for j in range(self.num_nodes):
                self.pheromone[i][j] *= (1 - evaporation_rate)
        for i in range(self.num_nodes - 1):
            self.pheromone[best_solution[i]][best_solution[i + 1]] += 1

# 主程序
if __name__ == "__main__":
    num_ants = 5
    num_nodes = 10
    colony = AntColony(num_ants, num_nodes)
    best_solution = [random.randint(0, num_nodes - 1) for _ in range(num_nodes)]
    colony.update_pheromone(best_solution)

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