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以下是为路径规划、选址优化问题提供的Matlab和Python代码示例，涵盖遗传算法（GA）、粒子群优化（PSO）和蚁群算法（ACO）的实现：

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1. 遗传算法（GA）路径规划（Matlab）

场景：解决TSP（旅行商问题）

matlab

	% 遗传算法参数设置
	numCities = 20;
	popSize = 100;
	maxGen = 500;
	mutationRate = 0.02;
	% 初始化城市坐标（随机生成）
	cities = rand(numCities, 2) * 100;
	% 定义适应度函数（路径长度倒数）
	fitnessFcn = @(x) 1 / pathLength(x, cities);
	% 运行遗传算法
	options = optimoptions('ga', 'PopulationSize', popSize, 'MaxGenerations', maxGen, 'MutationFcn', @mutationAdaptive);
	[bestRoute, ~] = ga(fitnessFcn, numCities, [], [], [], [], 1, numCities, [], options);
	% 绘制结果
	plot(cities(:,1), cities(:,2), 'o');
	hold on;
	plot(cities([bestRoute, bestRoute(1)],1), cities([bestRoute, bestRoute(1)],2), '-r');
	title('GA路径规划结果');
	% 路径长度计算函数
	function len = pathLength(route, cities)
	len = 0;
	for i = 1:length(route)-1
	len = len + norm(cities(route(i),:) - cities(route(i+1),:));
	end
	end

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2. 粒子群优化（PSO）选址优化（Python）

场景：优化仓库位置以最小化运输成本

python

	import numpy as np
	import matplotlib.pyplot as plt
	# 参数设置
	num_particles = 30
	max_iter = 100
	demand_points = np.random.rand(10, 2) * 100 # 需求点坐标
	# PSO初始化
	particles = np.random.rand(num_particles, 2) * 100
	velocities = np.random.rand(num_particles, 2) * 0.1
	pbest = particles.copy()
	gbest = particles[np.argmin([cost(p, demand_points) for p in particles])]
	# 迭代优化
	for _ in range(max_iter):
	for i in range(num_particles):
	# 更新速度和位置
	velocities[i] = 0.5 * velocities[i] + 0.8 * np.random.rand(2) * (pbest[i] - particles[i]) + 0.2 * np.random.rand(2) * (gbest - particles[i])
	particles[i] += velocities[i]
	# 更新个体最优和全局最优
	if cost(particles[i], demand_points) < cost(pbest[i], demand_points):
	pbest[i] = particles[i]
	if cost(particles[i], demand_points) < cost(gbest, demand_points):
	gbest = particles[i]
	# 成本函数（欧氏距离总和）
	def cost(warehouse, points):
	return np.sum(np.sqrt(np.sum((points - warehouse)**2, axis=1)))
	# 可视化
	plt.scatter(demand_points[:,0], demand_points[:,1], c='red', label='需求点')
	plt.scatter(gbest[0], gbest[1], c='blue', marker='*', s=200, label='最优仓库位置')
	plt.legend(); plt.show()

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3. 蚁群算法（ACO）路径规划（Python）

场景：解决TSP问题

python

	import numpy as np
	# 参数设置
	num_ants = 20
	num_iterations = 100
	alpha = 1.0 # 信息素重要程度
	beta = 2.0 # 启发式信息重要程度
	evaporation = 0.5
	# 初始化信息素矩阵
	cities = np.random.rand(20, 2) * 100
	distance_matrix = np.sqrt(np.sum((cities[:, np.newaxis] - cities)**2, axis=2))
	pheromone = np.ones_like(distance_matrix)
	# ACO主循环
	for _ in range(num_iterations):
	paths = []
	for _ in range(num_ants):
	path = [0] # 起点
	unvisited = set(range(1, len(cities)))
	while unvisited:
	current = path[-1]
	probs = []
	for city in unvisited:
	heuristic = 1 / distance_matrix[current, city]
	prob = (pheromone[current, city]**alpha) * (heuristic**beta)
	probs.append(prob)
	probs = np.array(probs) / sum(probs)
	next_city = np.random.choice(list(unvisited), p=probs)
	path.append(next_city)
	unvisited.remove(next_city)
	paths.append(path)
	# 更新信息素
	pheromone *= evaporation
	for path in paths:
	path_length = sum(distance_matrix[path[i], path[i+1]] for i in range(len(path)-1))
	for i in range(len(path)-1):
	pheromone[path[i], path[i+1]] += 1 / path_length
	# 输出最优路径
	best_path = min(paths, key=lambda x: sum(distance_matrix[x[i], x[i+1]] for i in range(len(x)-1)))
	print("最优路径:", best_path)

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关键说明

1. 算法选择：
   • GA：适合离散问题（如TSP），全局搜索能力强。
   • PSO：适合连续空间优化（如选址），收敛速度快。
   • ACO：适合动态路径问题，依赖信息素更新机制。
2. 扩展建议：
   • 添加约束条件（如容量限制、障碍物避障）。
   • 使用更高效的数据结构（如邻接矩阵）加速距离计算。
3. 工具库推荐：
   • Python：DEAP（GA）、PySwarms（PSO）
   • Matlab：Global Optimization Toolbox（内置GA和PSO）

如需特定场景的完整实现（如多仓库选址或动态路径规划），可进一步说明需求！ 🐜🌍