多目标金鹰优化算法代码

多目标金鹰优化算法的代码如下所示：

# coding=utf-8

import random
import numpy as np

class MOFAGWO():
    def __init__(self, func, dim, size_pop=50, max_iter=500, ub=1, lb=0.0001, wmax=0.9, wmin=0.4, c1=2, c2=2):
        self.func = func
        self.dim = dim
        self.size_pop = size_pop
        self.max_iter = max_iter
        self.ub = ub
        self.lb = lb
        self.wmax = wmax
        self.wmin = wmin
        self.c1 = c1
        self.c2 = c2
        self.X = np.random.uniform(low=self.lb, high=self.ub, size=(self.size_pop, self.dim))
        self.V = np.zeros((self.size_pop, self.dim))
        self.Y = np.zeros((self.size_pop, 2))
        for i in range(self.size_pop):
            self.Y[i] = self.func(self.X[i])
        self.Ygb = np.min(self.Y, axis=0)
        self.Ypb = self.Ygb.copy()
        self.Xpb = self.X[np.argmin(self.Y[:, 0]), :]
        self.Xgb = self.Xpb.copy()

    def evolve(self):
        for step in range(self.max_iter):
            w = self.wmax - (self.wmax - self.wmin) * step / self.max_iter
            a = 2 - 2 * step / self.max_iter
            for i in range(self.size_pop):
                r1 = random.random()
                r2 = random.random()
                A1 = 2 * a * r1 - a
                C1 = 2 * r2
                D_alpha = abs(C1 * self.Xgb - self.X[i, :])
                X1 = self.Xgb - A1 * D_alpha
                r1 = random.random()
                r2 = random.random()
                A2 = 2 * a * r1 - a
                C2 = 2 * r2
                D_beta = abs(C2 * self.Xpb - self.X[i, :])
                X2 = self.Xpb - A2 * D_beta
                r1 = random.random()
                r2 = random.random()
                A3 = 2 * a * r1 - a
                C3 = 2 * r2
                D_delta = abs(C3 * self.X[i, :] - (self.Xpb + self.Xgb) / 2)
                X3 = (self.Xpb + self.Xgb) / 2 - A3 * D_delta
                self.V[i, :] = w * self.V[i, :] + self.c1 * random.random() * (X1 - self.X[i, :]) + self.c2 * random.random() * (X2 - self.X[i, :]) + self.c2 * random.random() * (X3 - self.X[i, :])
                self.X[i, :] = self.X[i, :] + self.V[i, :]
                self.X[i, :] = np.clip(self.X[i, :], self.lb, self.ub)
                self.Y[i] = self.func(self.X[i])
                if self.Y[i, 0] < self.Ypb[0]:
                    self.Xpb = self.X[i, :].copy()
                    self.Ypb = self.Y[i, :].copy()
                    if self.Ypb[0] < self.Ygb[0]:
                        self.Xgb = self.Xpb.copy()
                        self.Ygb = self.Ypb.copy()

    def result(self):
        return self.Xgb, self.Ygb

其中，`func` 是目标函数，`dim` 是变量维度，`size_pop` 是种群大小，`max_iter` 是最大迭代次数，`ub` 和 `lb` 分别是变量的上界和下界，`wmax` 和 `wmin` 分别是惯性权重的最大值和最小值，`c1` 和 `c2` 是学习因子。`evolve` 函数是算法的核心，用于进化优化过程。`result` 函数用于返回最优的解和目标函数值。