混合粒子群算法求解tsp源码

### 混合粒子群算法求解旅行商问题(TSP)

混合粒子群优化(Particle Swarm Optimization, PSO)结合其他启发式方法可以有效提升解决TSP的能力。下面展示了一个简单的Python实现案例，该实例融合了局部搜索策略来增强PSO的效果。

import numpy as np

class Hybrid_PSO_TSP:
    def __init__(self, cities_coordinates, num_particles=30, max_iter=100):
        """
        初始化函数
        
        参数:
            cities_coordinates (list of tuples): 城市坐标列表 [(x1,y1),(x2,y2),...]
            num_particles      : 粒子数量
            max_iter           : 迭代次数上限
        """
        self.cities = cities_coordinates
        self.num_cities = len(cities_coordinates)
        self.particles_position = []
        self.particles_velocity = []
        self.personal_best_positions = []  # 记录个体历史最佳位置
        self.global_best_position = None   # 种群全局最优解
        self.max_iterations = max_iter
        self.population_size = num_particles
    
    def calculate_distance(self, path):
        """计算给定路径的距离"""
        distance = sum([np.linalg.norm(np.array(self.cities[path[i]]) - np.array(self.cities[path[(i+1)%len(path)]])) \
                        for i in range(len(path)-1)])
        return round(distance, 2)

    def initialize_population(self):
        """初始化种群"""
        for _ in range(self.population_size):
            random_path = list(range(self.num_cities))
            np.random.shuffle(random_path)
            self.particles_position.append(random_path.copy())
            self.personal_best_positions.append(random_path.copy())
            
            velocity_vector = [0]*self.num_cities
            self.particles_velocity.append(velocity_vector)

    def update_particle(self, particle_index):
        """更新单个粒子的位置和速度"""

        current_pos = self.particles_position[particle_index].copy()
        
        # 更新速度部分省略...
        
        new_position = []

        # 应用交换算子或其他变异操作...

        if self.calculate_distance(new_position)<self.calculate_distance(current_pos):
            self.particles_position[particle_index]=new_position
            
            if self.calculate_distance(new_position)\
               <self.calculate_distance(self.personal_best_positions[particle_index]):
                self.personal_best_positions[particle_index]=new_position
                
                if not any((gbp==new_position).all()\
                           for gbp in self.global_best_position or self.global_best_position is None):
                    self.global_best_position=new_position
                    
    def run(self):
        """执行主要逻辑循环迭代寻优过程"""
        best_distances_history = []

        self.initialize_population()

        for iteration in range(self.max_iterations):

            for idx in range(self.population_size):
                self.update_particle(idx)

            best_distances_history.append(
                min([self.calculate_distance(p) for p in self.particles_position]))

        return {'best_solution': self.global_best_position,
                'history_of_best_fitness_values': best_distances_history}

此代码片段提供了一种基本框架用于构建基于PSO的TSP解决方案[^2]。为了使上述程序更加完善并适用于实际场景，还需要加入具体的粒子更新机制以及可能涉及到的一些额外约束条件处理等内容。