用python写一个粒子群算法在一堆节点中求两点最短距离的算法

粒子群算法(Particle Swarm Optimization, PSO)是一种进化算法, 它通过模拟群体中个体之间的相互作用来寻找全局最优解.

下面是一个使用Python实现的粒子群算法来求解在一堆节点中两点最短距离的示例代码:

import numpy as np

class Particle:
    def __init__(self, nodes):
        self.nodes = nodes
        self.n = len(nodes)
        self.position = np.random.permutation(self.n)
        self.pbest = self.position
        self.fitness = float('inf')
        self.update_fitness()
        
    def update_fitness(self):
        # 计算距离并更新适应度
        dist = 0
        for i in range(self.n - 1):
            dist += np.sqrt(np.sum((self.nodes[self.position[i]] - self.nodes[self.position[i + 1]]) ** 2))
        self.fitness = dist

class PSO:
    def __init__(self, nodes, n_particles, max_iter):
        self.nodes = nodes
        self.n = len(nodes)
        self.n_particles = n_particles
        self.max_iter = max_iter
        self.particles = [Particle(nodes) for _ in range(n_particles)]
        self.gbest = self.particles[0].pbest
        self.gbest_fitness = float('inf')
        self.w = 0.7
        self.c1 = 1.5
        self.c2 = 2.0
        
    def update_gbest(self):
        # 更新全局最优解
        for particle in self.particles:
            if particle.fitness < self.gbest_fitness:
                self.gbest = particle.pbest
                self.gbest_fitness = particle.fitness
                
    def run(self):
        for i in range(self.max_iter):
            for particle in self.particles:
                r1 = np.random.rand()
                r2 = np.random.rand()
                # 计算速度和位置
                particle.velocity = self.w * particle.velocity + \
                                    self.c1 * r1 * (particle.pbest - particle.position) + \
                                    self.c2 * r2 * (self.gbest - particle.position)
                particle.position = particle.position + particle.velocity
                particle.position = np.