jupyter 蚁群算法代码

Jupyter Notebook是一个交互式的笔记本环境，常用于数据科学、机器学习等领域，它支持Python和其他多种编程语言。蚂蚁群优化（Ant Colony Optimization, ACO）是一种模拟蚁群行为的搜索算法，通常用于解决组合优化问题。

在Jupyter中编写蚂蚁群算法代码，首先需要导入必要的库，如`networkx`（图论库）、`deap`（遗传算法库）以及自定义的蚂蚁操作函数等。以下是一个简单的例子：

import numpy as np
import networkx as nx
from deap import base, creator, tools

# 创建蚂蚁类
class Ant:
    def __init__(self, graph, pheromone):
        self.position = None
        self.pheromone_trail = pheromone.copy()

    # 爬行选择下一个节点
    def move(self, alpha, beta):
        choices = [(n, self.pheromone_trail[n] ** alpha) for n in self.graph.neighbors(self.position)]
        total = sum(choice[1] for choice in choices)
        r = np.random.rand()
        cumsum = 0
        next_node = None
        for node, prob in choices:
            cumsum += prob
            if r < cumsum / total:
                next_node = node
                break
        return next_node

# 定义蚁群算法核心部分
def ant_colony_optimization(graph, iterations, alpha, beta):
    pheromone = np.ones(len(graph.nodes))
    population_size = 50
    ants = [Ant(graph, pheromone.copy()) for _ in range(population_size)]

    # 主循环
    for _ in range(iterations):
        for ant in ants:
            ant.move(alpha, beta)
            graph[ant.position][next_node] += 1  # 更新pheromone trail
            best_ant = max(ants, key=lambda a: a.pheromone_trail[a.position])
        
        # 信息素更新（退火）
        evaporation_rate = 0.9  # 信息素挥发率
        pheromone *= (1 - evaporation_rate)
        pheromone[best_ant.position] += 1 / len(ants)  # 最佳路径加权增强

# 示例使用
graph = nx.grid_2d_graph(10, 10)
ant_colony_optimization(graph, 100, 1, 2)  # 参数调整以适应问题规模