多变量多目标的遗传算法程序

这是我在解决电梯动力学参数写的简单遗传算法（程序带目标函数值、适应度值计算，但是我的适应度函数因为目标函数的计算很特殊，一起放在了程序外面计算,在此不提供）。头文件：//CMVSOGA.h:mainheaderfilefortheCMVSOGA.cpp//本来想使用链表里面套链表的，程序调试比较麻烦，改为种群用链表表示//染色体固定为16的方法。#if!defined(AFX_CMVSOGA_H__45BECA_61EB_4A0E_9746_9A94D1CCF767__INCLUDED_)#defineAFX_CMVSOGA_H__45BECA_61EB_4A0E_9746_9A94D1CCF767__INCLUDED#if_MSC_VER>1000#pragmaonce#endif//_MSC_VER>1000#include"Afxtempl.h"#definevariablenum16classCMVSOGA{public:CMVSOGA();voidselectionoperator();voidcrossoveroperator();voidmutationoperator();voidinitialpopulation(int,int,double,double,double*,double*);//种群初始化voidgeneratenextpopulation();//生成下一代种群voidevaluatepopulation();//评价个体，求最佳个体voidcalculateobjectvalue();//计算目标函数值voidcalculatefitnessvalue();//计算适应度函数值voidfindbestandworstindividual();//寻找最佳个体和最差个体voidperformevolution();voidGetResult(double*);voidGetPopData(double**);voidSetValueData(double*);voidmaxandexpectation();private:structindividual{doublechromosome[variablenum];//染色体编码长度应该为变量的个数doublevalue;doublefitness;//适应度};doublevariabletop[variablenum];//变量值doublevariablebottom[variablenum];//变量值intpopsize;//种群大小//intgeneration;intbest_index;intworst_index;//世代数doublecrossoverrate;doublemutationrate;intmaxgeneration;//交叉率//变异率//最大世代数structindividualbestindividual;//最佳个体structindividualworstindividual;//最差个体structindividualcurrent;//当前个体structindividualcurrent1;//当前个体structindividualcurrentbest;//当前最佳个体CList<structindividual,structindividual&>population;//种群CList<structindividual,structindividual&>newpopulation;//新种群CList<double,double>cfitness;//存储适应度值//doublemaxfitness;//doubleminfitness;//doubleavefitness;//怎样使链表的数据是一个结构体主要是想把种群作成链表。节省空间。};#endif执行文件//CMVSOGA.cpp:implementationfile//#include"stdafx.h"#include"CMVSOGA.h"#include"math.h"#include"stdlib.h"#ifdef_DEBUG#definenewDEBUG_NEW#undefTHIS_FILEstaticcharTHIS_FILE[]=__FILE__;#endif///////////////////////////////////////////////////////////////////////////////CMVSOGA.cppvoidCMVSOGA::initialpopulation(intps,intgen,doublecr,doublemr,double*xtop,double*xbottom)//第一步，初始化。{inti,j;popsize=ps;maxgeneration=gen;crossoverrate=cr;mutationrate=mr;for(i=0;i<variablenum;i++){variabletop[i]=xtop[i];variablebottom[i]=xbottom[i];}srand((unsigned)time(NULL));for(i=0;i<popsize;i++){for(j=0;j<variablenum;j++){current.chromosome[j]=double(rand()%1000)/1000*(variabletop[j]-variablebottom[j])+variablebottom[j];}current.fitness=0;current.value=0;population.InsertAfter(population.Findlndex(i),current);〃除了初始化使用insertafter外，其他的用setat命令。}}voidCMVSOGA::generatenextpopulation()〃第三步，生成下一代。{selectionoperator();crossoveroperator();mutationoperator();}voidCMVSOGA::evaluatepopulation()//第二步，评价个体，求最佳个体{//calculateobjectvalue();calculatefitnessvalue();//在此步中因该按适应度值进行排序.链表的排序.findbestandworstindividual();}voidCMVSOGA::calculateobjectvalue()//计算函数值，应该由外部函数实现。主要因为目标函数很复杂。{inti,j;doublex[variablenum];for(i=0;i<popsize;i++){current=population.GetAt(population.FindIndex(i));current.value=0;//使用外部函数进行，在此只做结果的传递。for(j=0;j<variablenum;j++){x[j]=current.chromosome[j];current.value=current.value+(j+1)*pow(x[j],4);}////使用外部函数进行，在此只做结果的传递。population.SetAt(population.FindIndex(i),current);}}voidCMVSOGA::mutationoperator()//对于浮点数编码，变异算子的选择具有决定意义。//需要guass正态分布函数，生成方差为sigma，均值为浮点数编码值c。{inti,j;doubler1,r2,p,sigma;//sigma高斯变异参数sigma=0.5;for(i=0;i<popsize;i++){current=population.GetAt(population.FindIndex(i));〃生成均值为current.chromosome,方差为sigma的高斯分布数srand((unsignedint)time(NULL));for(j=0;j<variablenum;j++){r1=double(rand()%1001)/1000;r2=double(rand()%1001)/1000;p=double(rand()%1000)/1000;if(p<mutationrate){//高斯变异current.chromosome[j]=(current.chromosome[j]+sigma*sqrt(-2*log(r1)/0.4323)*sin(2*3.1415926*r2));if(current.chromosome[j]>variabletop[j]){current.chromosome[j]=variabletop[j];}if(current.chromosome[j]<variablebottom[j]){current.chromosome[j]=variablebottom[j];}}}population.SetAt(population.FindIndex(i),current);}}voidCMVSOGA::selectionoperator()//从当前个体中按概率选择新种群,应该加一个复制选择,提高种群的平均适应度//第二次循环出错{inti,j,pindex=0;doublep,pc,sum=0;i=0;j=0;pindex=0;p=0;pc=0;sum=0.001;newpopulation.RemoveAll();cfitness.RemoveAll();//链表排序//population.SetAt(population.FindIndex(0),current);//多余代码for(i=1;i<popsize;i++){current=population.GetAt(population.FindIndex(i));for(j=0;j<i;j++)//从小到大用before排列。{current1=population.GetAt(population.Findindex(j));//临时借用变量if(current.fitness<=current1.fitness){population.insertBefore(population.Findindex(j),current);population.RemoveAt(population.Findindex(i+1));break;}}//m=population.GetCount();}//链表排序for(i=0;ivpopsize;i++)〃求适应度总值，以便归一化，是已经排序好的链。{current=population.GetAt(population.Findindex(i));sum+=current.fitness;}for(i=0;ivpopsize;i++)〃归一化{current=population.GetAt(population.Findindex(i));current.fitness=current.fitness/sum;cfitness.insertAfter(cfitness.Findindex(i),current.fitness);for(i=1;ivpopsize;i++)〃概率值从小到大；{current.fitness=cfitness.GetAt(cfitness.FindIndex(i-1))+cfitness.GetAt(cfitness.FindIndex(i));//归一化cfitness.SetAt(cfitness.FindIndex(i),current.fitness);population.SetAt(population.FindIndex(i),current);}for(i=0;ivpopsize;)〃轮盘赌概率选择。本段还有问题。{p=double(rand()%1000)/1000+0.0001;//随机生成概率pindex=0;//遍历索引pc=cfitness.GetAt(cfitness.FindIndex(0));while(p>=pc&&pindexvpopsize)//问题所在。{pc=cfitness.GetAt(cfitness.FindIndex(pindex));pindex++;}〃必须是从index~popsize，选择高概率的数。即大于概率p的数应该被选择，选择不满则进行下次选择。for(j=popsize-1;jvpindex&&ivpopsize;j--){newpopulation.InsertAfter(newpopulation.FindIndex(0),population.GetAt(population.FindIndex(j)));i++;}}for(i=0;ivpopsize;i++){population.SetAt(population.FindIndex(i),newpopulation.GetAt(newpopulation.FindIndex(i)));}//j=newpopulation.GetCount();//j=population.GetCount();newpopulation.RemoveAll();}//current变化后，以上没有问题了。voidCMVSOGA::crossoveroperator()//非均匀算术线性交叉，浮点数适用,alpha,beta是(0，1)之间的随机数〃对种群中两两交叉的个体选择也是随机选择的。也可取beta=1-alpha;〃current的变化会有一些改变。{inti,j;doublealpha,beta;CList<int,int>index;intpoint,temp;doublep;srand((unsigned)time(NULL));for(i=0;ivpopsize;i++)〃生成序号{index.InsertAfter(index.FindIndex(i),i);}for(i=0;ivpopsize;i++)〃打乱序号{point=rand()%(popsize-1);temp=index.GetAt(index.FindIndex(i));index.SetAt(index.FindIndex(i),index.GetAt(index.FindIndex(point)));index.SetAt(index.FindIndex(point),temp);}for(i=0;ivpopsize-1;i+=2){//按顺序序号,按序号选择两个母体进行交叉操作。p=double(rand()%1000)/1000.0;if(pvcrossoverrate){alpha=double(rand()%1000)/1000.0;beta=double(rand()%1000)/1000.0;current=population.GetAt(population.FindIndex(index.GetAt(index.FindIndex(i))));current1=population.GetAt(population.FindIndex(index.GetAt(index.FindIndex(i+1))));〃临时使用currentl代替for(j=0;jvvariablenum;j++){//交叉current.chromosome[j]=(1-alpha)*current.chromosome[j]+beta*current1.chromosome[j];if(current.chromosome[j]>variabletop[j])//判断是否超界.{current.chromosome[j]=variabletop[j];}if(current.chromosome[j]vvariablebottom[j]){current.chromosome[j]=variablebottom[j];}current1.chromosome[j]=alpha*current.chromosome[j]+(1-beta)*current1.chromosome[j];if(current1.chromosome[j]>variabletop[j]){current1.chromosome[j]=variabletop[j];}if(current1.chromosome[j]<variablebottom[j]){current1.chromosome[j]=variablebottom[j];}}//回代}newpopulation.InsertAfter(newpopulation.FindIndex(i),current);newpopulation.InsertAfter(newpopulation.FindIndex(i),current1);}j=newpopulation.GetCount();for(i=0;i<popsize;i++){population.SetAt(population.FindIndex(i),newpopulation.GetAt(newpopulation.FindIndex(i)));}newpopulation.RemoveAll();}voidCMVSOGA::findbestandworstindividual(){inti;bestindividual=population.GetAt(population.FindIndex(best_index));worstindividual=population.GetAt(population.FindIndex(worst_index));for(i=1;i<popsize;i++){current=population.GetAt(population.FindIndex(i));if(current.fitness>bestindividual.fitness){bestindividual=current;best_index=i;}elseif(current.fitness<worstindividual.fitness){worstindividual=current;worst_index=i;}}population.SetAt(population.FindIndex(worst_index),population.GetAt(population.FindIndex(best_index)));//用最好的替代最差的。使用这个之后结果相差很大。不收敛。if(maxgeneration==0){currentbest=bestindividual;}else{if(bestindividual.fitness>=currentbest.fitness){currentbest=bestindividual;}}}voidCMVSOGA::calculatefitnessvalue()//适应度函数值计算，关键是适应度函数的设计//current变化，这段程序变化较大，特别是排序。{inti;doubletemp;〃alpha,beta;〃适应度函数的尺度变化系数doublecmax=100;for(i=0;i<popsize;i++){current=population.GetAt(population.FindIndex(i));if(current.value<cmax){temp=cmax-current.value;}else{temp=0.0;}/*if((population[i].value+cmin)>0.0){temp=cmin+population[i].value;}else{temp=0.0;}*/current.fitness=temp;population.SetAt(population.FindIndex(i),current);}}voidCMVSOGA::performevolution()//演示评价结果，有冗余代码,current变化，程序应该改变较大{if(bestindividual.fitness>currentbest.fitness){currentbest=population.GetAt(population.FindIndex(best_index));}else{population.SetAt(population.FindIndex(worst_index),currentbest);}}voidCMVSOGA::GetResult(double*Result){inti;for(i=0;i<variablenum;i++){Result[i]=currentbest.chromosome[i];}Result[i]=currentbest.value;}CMVSOGA::CMVSOGA(){best_index=0;worst_index=0;crossoverrate=0;//交叉率mutationrate=0;//变异率maxgeneration=0;}voidCMVSOGA::GetPopData(double**PopData)//不能保证每次计算结果都对,为什么?{inti,j;for(i=0;i<popsize;i++){current=population.GetAt(population.FindIndex(i));for(j=0;j<variablenum;j++){PopData[i][j]=current.chromosome[j];}}}voidCMVSOGA::SetValueData(double*ValueData){intj;for(j=0;j<popsize;j++){current=population.GetAt(population.FindIndex(j));current.value=ValueData[j];population.SetAt(population.FindIndex(j),current);}}DLG中调用代码：voidCMVSOFGADlg::OncalNoGuass(){//TODO:AddyourcontrolnotificationhandlercodehereUpdateData(true);UINTi;UINTgeneration;doubletopx[16],bottomx[16];doubleresultx[17];////////////////////////////////////double**PopData;double*PopValue;PopData=newdouble*[m_popsize];for(i=0;ivm_popsize;i++)〃〃〃〃〃〃〃〃/〃〃〃〃变量数目，程序更改时注意/////////////{PopData[i]=newdouble[16];}PopValue=newdouble[m_popsize];//////////////////////////////////////////////////初始化/////////////CMVSOGAcdata;m_result=0;topx[0]=1.28;topx[1]=1.28;topx[2]=1.28;topx[3]=1.28;topx[4]=1.28;topx[5]=1.28;topx[6]=1.28;topx[7]=1.28;topx[8]=1.28;topx[9]=1.28;topx[10]=1.28;topx[11]=1.28;topx[12]=1.28;topx[13]=1.28;topx[14]=1.28;topx[15]=1.28;bottomx[0]=-1.28;bottomx[1]=-1.28;bottomx[2]=-1.28;bottomx[3]=-1.28;bottomx[4]=-1.28;bottomx[5]=-1.28;bottomx[6]=-1.28;bottomx[7]=-1.28;bottomx[8]=-1.28;bottomx[9]=-1.28;bottomx[10]=-1.28;bottomx[11]=-1.28;bottomx[12]=-1.28;bottomx[13]=-1.28;bottomx[14]=-1.28;bottomx[15]=-1.28;cdata.initialpopulation(m_popsize,m_generation,m_csrate,m_mrate,topx,bottomx);cdata.GetPopData(PopData);TargetFunctionCal(PopData,PopValue);cdata.SetValueData