有没有遗传粒子群多目标优化算法代码

遗传粒子群（GPSO）算法是一种组合了遗传算法和粒子群算法的优化算法，可以用于多目标优化问题。以下是一个基于Python的GPSO多目标优化算法的代码示例：

import numpy as np

class GPSO:
    def __init__(self, objective_func, num_variables, num_particles, num_objectives, max_iter, w, c1, c2):
        self.objective_func = objective_func
        self.num_variables = num_variables
        self.num_particles = num_particles
        self.num_objectives = num_objectives
        self.max_iter = max_iter
        self.w = w
        self.c1 = c1
        self.c2 = c2
        self.particles = np.random.rand(num_particles, num_variables)
        self.velocities = np.zeros((num_particles, num_variables))
        self.pbest = self.particles.copy()
        self.pbest_scores = np.zeros(num_particles)
        self.gbest = np.zeros(num_variables)
        self.gbest_scores = np.zeros(num_objectives)
        
    def optimize(self):
        for i in range(self.max_iter):
            for j in range(self.num_particles):
                scores = self.objective_func(self.particles[j])
                if i == 0:
                    self.pbest_scores[j] = scores
                if np.all(scores < self.pbest_scores[j]):
                    self.pbest_scores[j] = scores
                    self.pbest[j] = self.particles[j].copy()
                if np.all(scores < self.gbest_scores):
                    self.gbest_scores = scores
                    self.gbest = self.particles[j].copy()
                r1 = np.random.rand(self.num_variables)
                r2 = np.random.rand(self.num_variables)
                self.velocities[j] = (self.w * self.velocities[j] +
                                      self.c1 * r1 * (self.pbest[j] - self.particles[j]) +
                                      self.c2 * r2 * (self.gbest - self.particles[j]))
                self.particles[j] = self.particles[j] + self.velocities[j]
        return self.gbest, self.gbest_scores

在上述代码中，objective_func是用于计算目标函数值的函数，num_variables是变量的数量，num_particles是粒子的数量，num_objectives是目标函数的数量，max_iter是迭代次数，w、c1和c2是算法的超参数，分别表示惯性权重、个体和全局学习因子。在optimize函数中，算法使用了标准的GPSO算法流程，包括初始化粒子、计算粒子适应值、更新粒子速度和位置等步骤。最终，算法返回全局最优解和对应的目标函数值。