建立一个PSO类实现PSO算法

好的，以下是一个基于类的PSO算法实现示例：

import numpy as np

class Particle:
    def __init__(self, dim, lb, ub):
        self.position = np.random.uniform(lb, ub, dim)
        self.velocity = np.zeros(dim)
        self.best_position = self.position.copy()
        self.best_cost = np.inf

class PSO:
    def __init__(self, cost_function, dim, num_particles, maxiter, lb, ub, w=0.8, c1=2, c2=2):
        self.cost_function = cost_function
        self.dim = dim
        self.num_particles = num_particles
        self.maxiter = maxiter
        self.lb = lb
        self.ub = ub
        self.w = w
        self.c1 = c1
        self.c2 = c2

        self.particles = [Particle(dim, lb, ub) for _ in range(num_particles)]
        self.g_best = np.zeros(dim)
        self.g_best_cost = np.inf

    def optimize(self):
        for i in range(self.maxiter):
            for j in range(self.num_particles):
                # 计算适应度值
                cost = self.cost_function(self.particles[j].position)

                # 更新个体历史最优位置和适应度值
                if cost < self.particles[j].best_cost:
                    self.particles[j].best_position = self.particles[j].position.copy()
                    self.particles[j].best_cost = cost

                # 更新全局历史最优位置和适应度值
                if cost < self.g_best_cost:
                    self.g_best = self.particles[j].position.copy()
                    self.g_best_cost = cost

                # 更新粒子速度和位置
                r1 = np.random.uniform(size=self.dim)
                r2 = np.random.uniform(size=self.dim)
                self.particles[j].velocity = self.w * self.particles[j].velocity \
                    + self.c1 * r1 * (self.particles[j].best_position - self.particles[j].position) \
                    + self.c2 * r2 * (self.g_best - self.particles[j].position)
                self.particles[j].position = self.particles[j].position + self.particles[j].velocity

                # 边界处理
                self.particles[j].position = np.maximum(self.particles[j].position, self.lb)
                self.particles[j].position = np.minimum(self.particles[j].position, self.ub)

        return self.g_best, self.g_best_cost

`Particle`类表示粒子，包含位置、速度、个体历史最优位置和适应度值等属性。`PSO`类表示整个粒子群算法，包含目标函数、变量数目、粒子数、最大迭代次数、变量下界和上界、惯性权重、加速常数等属性，以及粒子列表、全局历史最优位置和适应度值等属性。`optimize`方法实现了算法的优化过程。