多目标灰狼优化算法python代码

以下是多目标灰狼优化算法的 Python 代码：

import numpy as np

# 定义灰狼优化算法的类
class MOGWO:
    def __init__(self, obj_func, num_var, num_obj, num_wolf=30, max_iter=500, lb=-100, ub=100, a=2):
        self.obj_func = obj_func  # 目标函数
        self.num_var = num_var  # 变量个数
        self.num_obj = num_obj  # 目标个数
        self.num_wolf = num_wolf  # 狼群大小
        self.max_iter = max_iter  # 最大迭代次数
        self.lb = lb  # 变量下界
        self.ub = ub  # 变量上界
        self.a = a  # 系数

    # 初始化狼群
    def init_wolf(self):
        self.wolf_pos = np.random.uniform(self.lb, self.ub, size=(self.num_wolf, self.num_var))
        self.wolf_fitness = np.zeros((self.num_wolf, self.num_obj))
        for i in range(self.num_wolf):
            self.wolf_fitness[i] = self.obj_func(self.wolf_pos[i])

    # 更新狼群位置
    def update_wolf(self, alpha_pos, beta_pos, delta_pos):
        a = 2 - 2 * self.iter / self.max_iter  # 计算系数a
        for i in range(self.num_wolf):
            r1 = np.random.rand(self.num_var)
            r2 = np.random.rand(self.num_var)
            A1 = 2 * a * r1 - a
            C1 = 2 * r2
            D_alpha = abs(C1 * alpha_pos - self.wolf_pos[i])
            X1 = alpha_pos - A1 * D_alpha

            r1 = np.random.rand(self.num_var)
            r2 = np.random.rand(self.num_var)
            A2 = 2 * a * r1 - a
            C2 = 2 * r2
            D_beta = abs(C2 * beta_pos - self.wolf_pos[i])
            X2 = beta_pos - A2 * D_beta

            r1 = np.random.rand(self.num_var)
            r2 = np.random.rand(self.num_var)
            A3 = 2 * a * r1 - a
            C3 = 2 * r2
            D_delta = abs(C3 * delta_pos - self.wolf_pos[i])
            X3 = delta_pos - A3 * D_delta

            self.wolf_pos[i] = (X1 + X2 + X3) / 3

    # 计算适应度值
    def calc_fitness(self):
        for i in range(self.num_wolf):
            self.wolf_fitness[i] = self.obj_func(self.wolf_pos[i])

    # 执行灰狼优化算法
    def optimize(self):
        self.init_wolf()
        for self.iter in range(self.max_iter):
            # 计算适应度值
            self.calc_fitness()
            # 排序
            sort_index = np.argsort(self.wolf_fitness[:, ])
            self.wolf_pos = self.wolf_pos[sort_index]
            self.wolf_fitness = self.wolf_fitness[sort_index]
            # 更新alpha、beta、delta位置
            alpha_pos = self.wolf_pos[]
            beta_pos = self.wolf_pos[1]
            delta_pos = self.wolf_pos[2]
            # 更新狼群位置
            self.update_wolf(alpha_pos, beta_pos, delta_pos)
        # 返回最优解
        return self.wolf_pos[], self.wolf_fitness[]

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