猎人猎物优化算法

猎人猎物优化算法

连续空调间的优化算法

import numpy as np
from tqdm import tqdm  # 进度条设置
import random
from matplotlib import rcParams
import math
import matplotlib; matplotlib.use('TkAgg')
from pylab import *
mpl.rcParams['font.sans-serif'] = ['SimHei']  # 指定默认字体
mpl.rcParams['axes.unicode_minus'] = False  # 解决保存图像是负号'-'显示为方块的问题

config = {
    "font.family": 'serif',
    "mathtext.fontset": 'stix',
    "font.serif": ['SimSun'],
}
rcParams.update(config)


class HPO(object):
    def __init__(self, m, T, lb, ub, R, C):
        self.M= m  # 种群个数
        self.T = T  # 迭代次数
        self.lb = lb
        self.ub = ub
        self.R = R  #行
        self.C = C   #列
        self.b = 0.1 #调节参数
    def init_x(self):
        x = np.random.uniform(self.lb, self.ub, (self.M, self.R, self.C))
        return x

    def fitness(self,x):
        """
             (x1-50)**2+(x2-50)**2
             :param x:
             :return:
             """
        # result = (x[0] - 50) ** 2 + (x[1] - 50) ** 2
        result = x[:, :, 0] ** 2 + x[:, :, 1] ** 2
        return result



    def main(self):
        x = self.init_x()
        fitness = self.fitness(x)
        fitness_best = fitness.min()
        x_num = list(fitness).index(fitness_best)
        x_best = x[x_num]

        fitness_best_list = []
        fitness_best_list.append(fitness_best)

        for it in range(self.T):
            C = 1-it*(0.98/self.T)
            R1 = np.random.choice([0, 1], (self.M, self.R, self.C))
            R3 = np.random.choice([0, 1], (self.M, self.R, self.C))
            R2 = np.random.rand(self.M)
            kbest = round(self.M*C)

            #R1  IDX  Z
            R1 = R1-C
            R1[R1 <=0] = 0
            R1[R1 > 0] = 1
            # R_IDX (p==0)   ~IDX = R1
            R_IDX = R1.copy()
            R_IDX = 1-R_IDX
            z = np.zeros((self.M, self.R, self.C))
            for i in  range(self.M):
                z[i] = R2[i] * R_IDX[i]
            z +=  R3*R1


            # #u
            # u=np.zeros((self.R,self.C))
            # for i in range(self.M):
            #     u += x[i]
            # u = u/self.M
            #
            # #Deuc
            # Deuc_par = x-u
            # # print(Deuc_par)
            # Deuc = np.zeros(self.M)
            # for i in range(self.M):
            #     Deuc[i] = np.sum(Deuc_par[i]**2)
            # Deuc = np.sqrt(Deuc)
            # max_index = np.argmax(Deuc)
            # P_pos = x[max_index]
            # x_new = np.zeros((self.M, self.R, self.C))
            # x_new=x+0.5*(2*C*z*P_pos-x+2*(1-C)*z*u-x)
            #
            # #kbest
            # kbest=round(self.M*C)
            R5 = np.random.rand()
            x_new = np.zeros((self.M, self.R, self.C))
            if 0<self.b:
                # u
                u = np.zeros((self.R, self.C))
                for i in range(self.M):
                    u += x[i]
                u = u / self.M

                # Deuc
                Deuc_par = x - u
                # print(Deuc_par)
                Deuc = np.zeros(self.M)
                for i in range(self.M):
                    Deuc[i] = np.sqrt(np.sum(Deuc_par[i] ** 2))
                kbest = round(self.M * C)
                Deuc_max = np.max(sorted((Deuc)[:kbest]))
                Deuc = Deuc[:kbest]
                num_par = np.where(Deuc == Deuc_max)
                num = num_par[0][0]
                P_pos = x[num]
                x_new = x + 0.5 * (2 * C * z * P_pos - x + 2 * (1 - C) * z * u - x)
            else:
               R4 = np.random.rand()
               for i in range(self.M):
                    x_new[i] = x_best+C*z*np.cos(2*np.pi*R4)*(x_best-x[i])
            x = x_new
            fitness = self.fitness(x)
            fitness_best_new = fitness.min()

            if fitness_best_new <= fitness_best:
                fitness_best = fitness_best_new
                x_num = list(fitness).index(fitness_best)
                x_best = x[x_num]
            else:
                fitness_best = fitness_best
            fitness_best_list.append(fitness_best)
            print(fitness_best)
        plt.plot(fitness_best_list, label='迭代曲线')
        plt.xlabel('迭代次数')
        plt.ylabel('适应度')
        plt.legend()
        plt.show()

if __name__ == '__main__':
    hpo = HPO(5,10,-10,10,1,2)
    hpo.main()