粒子群算法python代码实现

以下是粒子群算法的Python代码实现：

import random

class Particle:
    def __init__(self, dim, minx, maxx):
        self.__pos = [random.uniform(minx, maxx) for i in range(dim)]
        self.__vel = [random.uniform(minx - maxx, maxx - minx) for i in range(dim)]
        self.__best_pos = self.__pos
        self.__fitness = float('inf')
        self.__best_fitness = float('inf')

    def update(self, func):
        self.__pos = [self.__pos[i] + self.__vel[i] for i in range(len(self.__pos))]
        self.__fitness = func(self.__pos)
        if self.__fitness < self.__best_fitness:
            self.__best_fitness = self.__fitness
            self.__best_pos = self.__pos

    def get_pos(self):
        return self.__pos

    def get_best_pos(self):
        return self.__best_pos

    def get_fitness(self):
        return self.__fitness

class PSO:
    def __init__(self, dim, size, iter_num, minx, maxx):
        self.__particles = [Particle(dim, minx, maxx) for i in range(size)]
        self.__gbest_fitness = float('inf')
        self.__gbest_pos = [random.uniform(minx, maxx) for i in range(dim)]
        self.__iter_num = iter_num

    def update(self, func):
        for i in range(self.__iter_num):
            for particle in self.__particles:
                particle.update(func)
                if particle.get_fitness() < self.__gbest_fitness:
                    self.__gbest_fitness = particle.get_fitness()
                    self.__gbest_pos = particle.get_pos()

    def get_gbest_pos(self):
        return self.__gbest_pos

    def get_gbest_fitness(self):
        return self.__gbest_fitness

这段代码实现了一个简单的粒子群算法，其中Particle类表示一个粒子，PSO类表示整个粒子群。在PSO类的update方法中，每个粒子都会根据当前的速度和位置更新自己，并更新全局最优解。最后，get_gbest_pos和get_gbest_fitness方法可以分别获取全局最优解的位置和适应度值。