多变量多目标的遗传算法程序.docx

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 : main header file for the CMVSOGA.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_)#define AFX_CMVSOGA_H_45BECA_61EB_4A0E_9746_9A94D1CCF767_INCLUDED_#if _MSC_VER 1000#pragma once#endif / _MSC_VER 1000#include Afxtempl.h#define variablenum 16class CMVSOGApublic:CMVSOGA();void selectionoperator();void crossoveroperator();void mutationoperator();void initialpopulation(int, int ,double ,double,double *,double *);/种群初始化void generatenextpopulation();/生成下一代种群void evaluatepopulation();/评价个体，求最佳个体void calculateobjectvalue();/计算目标函数值void calculatefitnessvalue();/计算适应度函数值void findbestandworstindividual();/寻找最佳个体和最差个体void performevolution();void GetResult(double *);void GetPopData(double *);void SetValueData(double *);void maxandexpectation();private:struct individualdouble chromosomevariablenum;/染色体编码长度应该为变量的个数double value; double fitness;/适应度;double variabletopvariablenum;/变量值double variablebottomvariablenum;/变量值int popsize;/种群大小/int generation;/世代数int best_index;int worst_index;double crossoverrate;/交叉率double mutationrate;/变异率int maxgeneration;/最大世代数struct individual bestindividual;/最佳个体struct individual worstindividual;/最差个体struct individual current; /当前个体struct individual current1; /当前个体struct individual currentbest;/当前最佳个体CList population;/种群CList newpopulation;/新种群CList cfitness;/存储适应度值/double maxfitness;/double minfitness;/double avefitness;/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 : implementation file/#include stdafx.h#include CMVSOGA.h#include math.h#include stdlib.h#ifdef _DEBUG#define new DEBUG_NEW#undef THIS_FILEstatic char THIS_FILE = _FILE_;#endif/ CMVSOGA.cppvoid CMVSOGA:initialpopulation(int ps, int gen ,double cr ,double mr,double *xtop,double *xbottom)/第一步，初始化。int i ,j;popsize=ps ;maxgeneration=gen;crossoverrate=cr;mutationrate =mr;for (i=0;ivariablenum;i+)variabletopi =xtopi;variablebottomi =xbottomi;srand( (unsigned)time( NULL ) );for(i=0;ipopsize;i+)for (j=0;jvariablenum ;j+)current.chromosomej=double(rand()%1000)/1000*(variabletopj-variablebottomj)+variablebottomj;current.fitness=0;current.value=0;population.InsertAfter(population.FindIndex(i),current);/除了初始化使用insertafter外,其他的用setat命令。void CMVSOGA:generatenextpopulation()/第三步，生成下一代。selectionoperator();crossoveroperator();mutationoperator();void CMVSOGA: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();void CMVSOGA: 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。int i,j; double xvariablenum;for (i=0; ipopsize; 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;jvariablenum;j+)xj=current.chromosomej;current.value=current.value+(j+1)*pow(xj,4);/使用外部函数进行，在此只做结果的传递。population.SetAt(population.FindIndex(i),current);void CMVSOGA: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。int i,j;double r1,r2,p,sigma;/sigma高斯变异参数sigma=0.5;for (i=0;ipopsize;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(unsigned int) time (NULL);for(j=0; jvariablenum; j+)r1 =double( rand()%1001)/1000;r2 = double(rand()%1001)/1000;p=double(rand()%1000)/1000;if(pvariabletopj)current.chromosomej=variabletopj;if (current.chromosomejvariablebottom j)current.chromosomej=variablebottom j;population.SetAt(population.FindIndex(i),current);void CMVSOGA: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int i,j,pindex=0;double p,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;ipopsize;i+)current=population.GetAt(population.FindIndex(i);for(j=0;ji;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;ipopsize;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;ipopsize; 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;ipopsize; 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;i=pc&pindexpopsize)/问题所在。pc=cfitness.GetAt(cfitness .FindIndex(pindex);pindex+;/必须是从indexpopsize，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;jpindex&ipopsize;j-)newpopulation.InsertAfter (newpopulation.FindIndex(0),population.GetAt (population.FindIndex(j);i+;for(i=0;ipopsize; 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。void CMVSOGA: 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。int i,j;double alpha,beta;CList index;int point,temp;double p;srand( (unsigned)time( NULL ) );for (i=0;ipopsize;i+)/生成序号index.InsertAfter (index.FindIndex(i),i);for (i=0;ipopsize;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;ipopsize-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 (pcrossoverrate) 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current1代替for(j=0;jvariabletopj)/判断是否超界.current.chromosomej=variabletopj;if (current.chromosomejvariabletopj)current1.chromosomej=variabletopj;if (current1.chromosomejvariablebottom j)current1.chromosomej=variablebottom j;/回代newpopulation.InsertAfter (newpopulation.FindIndex(i),current);newpopulation.InsertAfter (newpopulation.FindIndex(i),current1);j=newpopulation.GetCount();for (i=0;ipopsize;i+)population.SetAt (population.FindIndex(i),newpopulation.GetAt (newpopulation.FindIndex(i);newpopulation.RemoveAll();void CMVSOGA: findbestandworstindividual( )int i;bestindividual=population.GetAt(population.FindIndex(best_index);worstindividual=population.GetAt(population.FindIndex(worst_index);for (i=1;ibestindividual.fitness)bestindividual=current;best_index=i;else if (current.fitness=currentbest.fitness)currentbest=bestindividual;void CMVSOGA: 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。int i;double temp;/alpha,beta;/适应度函数的尺度变化系数double cmax=100;for(i=0;ipopsize;i+)current=population.GetAt(population.FindIndex(i);if(current.value0.0) temp=cmin+populationi.value;elsetemp=0.0; */current.fitness=temp;population.SetAt(population.FindIndex(i),current);void CMVSOGA: 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.fitnesscurrentbest.fitness)currentbest=population.GetAt(population.FindIndex(best_index);elsepopulation.SetAt(population.FindIndex(worst_index),currentbest);void CMVSOGA:GetResult(double *Result)int i;for (i=0;ivariablenum;i+)Resulti=currentbest.chromosomei;Resulti=currentbest.value;CMVSOGA:CMVSOGA()best_index=0;worst_index=0;crossoverrate=0;/交叉率mutationrate=0;/变异率maxgeneration=0;void CMVSOGA:GetPopData(double *PopData) /不能保证每次计算结果都对,为什么?int i,j;for (i=0;ipopsize;i+)current=population.GetAt(population.FindIndex(i);for (j=0;jvariablenum;j+)PopDataij=current.chromosomej;void CMVSOGA:SetValueData(double *ValueData)int j;for (j=0;jpopsize;j+) current=population.GetAt(population.FindIndex(j);current.value=ValueDataj;population.SetAt(population.FindIndex(j),current);DLG中调用代码：void CMVSOFGADlg:OncalNoGuass() / TODO: Add your control notification handler code hereUpdateData(true);UINT i;UINT generation;double topx16, bottomx16;double resultx17;/double * PopData;double *PopValue;PopData = new double*m_popsize;for(i=0; im_popsize; i+)/变量数目，程序更改时注意/PopDatai = new double16;PopValue=new double m_popsize;/初始化/CMVSOGA cdata;m_result=0;topx0=1.28;topx1=1.28;topx2=1.28;topx3=1.28;topx4=1.28;topx5=1.28;topx6=1.28;topx7=1.28;topx8=1.28;topx9=1.28;topx10=1.28;topx11=1.28;topx12=1.28;topx13=1.28;topx14=1.28;topx15=1.28;bottomx0=-1.28;bottomx1=-1.28;bottomx2=-1.28;bottomx3=-1.28;bottomx4=-1.28;bottomx5=-1.28;bottomx6=-1.28;bottomx7=-1.28;bottomx8=-1