目标函数

maxf(x1,x2)=21.5+x1sin(4πx1)+x2sin(20πx2)
−3.0≤x1≤12.1
4.1≤x3≤5.8

编码方式

本程序采用的是二进制编码精确到小数点后五位，经过计算可知对于 x1 其编码长度为18，对于 x2 其编码长度为15，因此每个基于的长度为33。

参数设置

种群大小 pop=100 ，交配池 Ppool=pop/2 ，变异概率 Pm=100 ，交叉概率 Pc=100 ，最大迭代次数 Gmax=300 ，自学习变异概率 Spm=0.05 。

算法步骤

设计的程序主要分为以下步骤：1、参数设置；2、种群初始化；3、用轮盘赌方法选择其中一半较好的个体作为父代；4、交叉和变异；5、更新最优解；6、对最有个体进行自学习操作；7结果输出。其算法流程图为：

算法结果

由程序输出可知其最终优化结果为38.85029， x1=11.625589，x2=5.725031 。
输出基因编码为[1 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 1 0 1 0 0 1 0 1 1 1 1]。

代码

import numpy as np
import random
import math
import copy

class Ind():
    def __init__(self):
        self.fitness = 0
        self.x = np.zeros(33)
        self.place = 0
        self.x1 = 0
        self.x2 = 0

def Cal_fit(x, upper, lower):   #计算适应度值函数
    Temp1 = 0
    for i in range(18):
        Temp1 += x[i] * math.pow(2, i)
    Temp2 = 0
    for i in range(18, 33, 1):
        Temp2 += math.pow(2, i - 18) * x[i]
    x1 = lower[0] + Temp1 * (upper[0] - lower[0])/(math.pow(2, 18) - 1)
    x2 = lower[1] + Temp2 * (upper[1] - lower[1])/(math.pow(2, 15) - 1)
    if x1 > upper[0]:
        x1 = random.uniform(lower[0], upper[0])
    if x2 > upper[1]:
        x2 = random.uniform(lower[1], upper[1])
    return 21.5 + x1 * math.sin(4 * math.pi * (x1)) + x2 * math.sin(20 * math.pi * x2)
def Init(G, upper, lower, Pop):    #初始化函数
    for i in range(Pop):
        for j in range(33):
            G[i].x[j] = random.randint(0, 1)
        G[i].fitness = Cal_fit(G[i].x, upper, lower)
        G[i].place = i
def Find_Best(G, Pop):
    Temp = copy.deepcopy(G[0])
    for i in range(1, Pop, 1):
        if G[i].fitness > Temp.fitness:
            Temp = copy.deepcopy(G[i])
    return Temp

def Selection(G, Gparent, Pop, Ppool):    #选择函数
    fit_sum = np.zeros(Pop)
    fit_sum[0] = G[0].fitness
    for i in range(1, Pop, 1):
        fit_sum[i] = G[i].fitness + fit_sum[i - 1]
    fit_sum = fit_sum/fit_sum.max()
    for i in range(Ppool):
        rate = random.random()
        Gparent[i] = copy.deepcopy(G[np.where(fit_sum > rate)[0][0]])

def Cross_and_Mutation(Gparent, Gchild, Pc, Pm, upper, lower, Pop, Ppool):  #交叉和变异
    for i in range(Ppool):
        place = random.sample([_ for _ in range(Ppool)], 2)
        parent1 = copy.deepcopy(Gparent[place[0]])
        parent2 = copy.deepcopy(Gparent[place[1]])
        parent3 = copy.deepcopy(parent2)
        if random.random() < Pc:
            num = random.sample([_ for _ in range(1, 32, 1)], 2)
            num.sort()
            if random.random() < 0.5:
                for j in range(num[0], num[1], 1):
                    parent2.x[j] = parent1.x[j]
            else:
                for j in range(0, num[0], 1):
                    parent2.x[j] = parent1.x[j]
                for j in range(num[1], 33, 1):
                    parent2.x[j] = parent1.x[j]
            num = random.sample([_ for _ in range(1, 32, 1)], 2)
            num.sort()
            num.sort()
            if random.random() < 0.5:
                for j in range(num[0], num[1], 1):
                    parent1.x[j] = parent3.x[j]
            else:
                for j in range(0, num[0], 1):
                    parent1.x[j] = parent3.x[j]
                for j in range(num[1], 33, 1):
                    parent1.x[j] = parent3.x[j]
        for j in range(33):
            if random.random() < Pm:
                parent1.x[j] = (parent1.x[j] + 1) % 2
            if random.random() < Pm:
                parent2.x[j] = (parent2.x[j] + 1) % 2

        parent1.fitness = Cal_fit(parent1.x, upper, lower)
        parent2.fitness = Cal_fit(parent2.x, upper, lower)
        Gchild[2 * i] = copy.deepcopy(parent1)
        Gchild[2 * i + 1] = copy.deepcopy(parent2)

def Choose_next(G, Gchild, Gsum, Pop):    #选择下一代函数
    for i in range(Pop):
        Gsum[i] = copy.deepcopy(G[i])
        Gsum[2 * i + 1] = copy.deepcopy(Gchild[i])
    Gsum = sorted(Gsum, key = lambda x: x.fitness, reverse = True)
    for i in range(Pop):
        G[i] = copy.deepcopy(Gsum[i])
        G[i].place = i

def Decode(x):            #解码函数
    Temp1 = 0
    for i in range(18):
        Temp1 += x[i] * math.pow(2, i)
    Temp2 = 0
    for i in range(18, 33, 1):
        Temp2 += math.pow(2, i - 18) * x[i]
    x1 = lower[0] + Temp1 * (upper[0] - lower[0]) / (math.pow(2, 18) - 1)
    x2 = lower[1] + Temp2 * (upper[1] - lower[1]) / (math.pow(2, 15) - 1)
    if x1 > upper[0]:
        x1 = random.uniform(lower[0], upper[0])
    if x2 > upper[1]:
        x2 = random.uniform(lower[1], upper[1])
    return x1, x2

def Self_Learn(Best, upper, lower, sPm, sLearn):  #自学习操作
    num = 0
    Temp = copy.deepcopy(Best)
    while True:
        num += 1
        for j in range(33):
            if random.random() < sPm:
                Temp.x[j] = (Temp.x[j] + 1)%2
        Temp.fitness = Cal_fit(Temp.x, upper, lower)
        if Temp.fitness > Best.fitness:
            Best = copy.deepcopy(Temp)
            num = 0
        if num > sLearn:
            break
    return Best

if __name__ == '__main__':
    upper = [12.1, 5.8]
    lower = [-3, 4.1]
    Pop = 100
    Ppool = 50
    G_max = 300
    Pc = 0.8
    Pm = 0.1
    sPm = 0.05
    sLearn = 20
    G = np.array([Ind() for _ in range(Pop)])
    Gparent = np.array([Ind() for _  in range(Ppool)])
    Gchild = np.array([Ind() for _ in range(Pop)])
    Gsum = np.array([Ind() for _ in range(Pop * 2)])
    Init(G, upper, lower, Pop)       #初始化
    Best = Find_Best(G, Pop)
    for k in range(G_max):
        Selection(G, Gparent, Pop, Ppool)       #使用轮盘赌方法选择其中50%为父代
        Cross_and_Mutation(Gparent, Gchild, Pc, Pm, upper, lower, Pop, Ppool)  #交叉和变异生成子代
        Choose_next(G, Gchild, Gsum, Pop)       #选择出父代和子代中较优秀的个体
        Cbest = Find_Best(G, Pop)
        if Best.fitness < Cbest.fitness:
            Best = copy.deepcopy(Cbest)        #跟新最优解
        else:
            G[Cbest.place] = copy.deepcopy(Best)
        Best = Self_Learn(Best, upper, lower, sPm, sLearn)
        print(Best.fitness)
    x1, x2 = Decode(Best.x)
    print(Best.x)
    print([x1, x2])