人工智能实验四

1、人工智能上机实验报告学号：1708090122姓名：陶浩所在系：信息学院班级:惠普测试171班实验名称：遗传算法的实现与应用实验日期2019年12月26日实验指导教师翌邂实验机房D2-602.实验目的：理解遗传算法的基本过程，掌握其选择-复制、交叉、变异三种运算。掌握基本遗传算法，会建立适应度函数，通过遗传算法的迭代实现优化问题。.实验内容及要求：实验内容：采用遗传算法求解以下函数的最大值：1(112)(122)(11)(11)其中2122采用基本粒子群算法求解以下函数的最小值：(在实现过程中体会迭代步数、粒子群规模、学习因子、惯性权重对结果的影响)30%2Ii=13算法设计(1)遗传算法中有

2、三种关于染色体的运算：选择-复制，交叉和变异，这三种运算被称为遗传操作或者遗传算子。/#includestdafx.h#include#include#include#include#include#include#include#defineLENGTH3usingnamespacestd;classchromosomepublic:intx1,x2;intvalue;doublefitness;voidassign(inta,intb)x1a;x2=b;cal_value();voidcal_value()value=x1LENGTH;value|=x2;voidcal_fitness()

3、fitness=100*(x1*x1-x2)*(x1*x2-x2)+(1-x1)*(1-x1);doubletotal_fitness=0;vectorpopulation2;vectortemp;intcop_counter=0;intselector=0;intand1=(1LENGTH)-1)LENGTH;intand2=(1LENGTH)-6;intvariation_percent=2;intrandom(intend)returnrand()%(end+1);/选择-复制voidselect_population()intcounter=0;intnew_sel=(selector

4、+1)%2;populationnew_sel.clear();while(counter=3)intpos=populationselector.size()-1;intr=random(total_fitness);intend=populationselector0.fitness;for(inti=1;i(int)populationselector.size();i+)if(r=end)pos=i-1;break;elseend+=populationselectori.fitness;populationnew_sel.push_back(populationselectorpos

5、);counter+;/交叉voidcop(chromosome&temp1,chromosome&temp2)chromosomenew1,new2;intpos=random(LENGTH*2-2);intv1=temp1.value;intv2=temp2.value;intlength=LENGTH*2;intnum1=(1pos)-1)(length-pos);intnum2=(1LENGTH,new_v1&and2);new2.assign(new_v2&and1)LENGTH,new_v2&and2);temp.push_back(new1);temp.push_back(new

6、2);/种群内随机进行交配voidcopulation()chromosometemp1,temp2;intremainder=4;intr1,r2;intnew_sel=(selector+1)%2;cop_counter=0;while(remainder)r1=random(remainder-1);temp1=populationnew_selr1;populationnew_sel.erase(populationnew_sel.begin()+r1);remainder-;r2=random(remainder-1);temp2=populationnew_selr2;popula

7、tionnew_sel.erase(populationnew_sel.begin()+r2);remainder-;cop(temp1,temp2);populationnew_sel=temp;temp.clear();/变异voidvariation()intnew_sel=(selector+1)%2;for(inti=0;i(int)populationnew_sel.size();i+)intrand=random(99)+1;if(rand=2)intpos=random(LENGTH*2-1)+1;intnum=1LENGTH;populationnew_seli.x2=pop

8、ulationnew_seli.value&and2;/更新适应度voidupdate_fitness()total_fitness=0;for(inti=0;i(int)populationselector.size();i+)populationselectori.cal_fitness();total_fitness+=populationselectori.fitness;boolcmp(chromosomea,chromosomeb)returna.fitnessb.fitness;voidinit()srand(unsigned)time(NULL);chromosomechr;c

9、hr.assign(-2,2);population0.push_back(chr);chr.assign(-1.048,1.048);population0.push_back(chr);chr.assign(-1,1);population0.push_back(chr);chr.assign(4.048,0.048);population0.push_back(chr);intmain()intcounter=0;init();intmaxn=0;intmaxvalue;while(countermaxn)maxn=populationselectorpopulationselector

10、.size()-1.fitness;maxvalue=populationselectorpopulationselector.size()-1.value;select_population();copulation();variation();selector=(selector+1)%2;counter+;printf(得到的x1和x2为：);printf(x1:%dx2:%dn,(maxvalue&and1)LENGTH,maxvalue&and2);doublex1=(maxvalue&and1)LENGTH,x2=maxvalue&and2;doublex;x=100*(x1*x1

11、-x2)*(x1*x2-x2)+(1-x1)*(1-x1);printf(采用遗传算法求解函数的最大值:x=%fn,x);运行结果：蜜C:W蜜C:WINDOWSsystem32cmd.exe得到的xl和工2为:kLTx2:2杲用遗存章法兼解函数的最大值：后56431000000请按任意键继续.(2)PSO流程更新各个粒子位置及速度以任意的位置和速度来初始化粒工评估各个粒子的适附值更新各个粒子位置及速度以任意的位置和速度来初始化粒工评估各个粒子的适附值更新pbestqgbest值满足终止条件开始结束packagemap;importjava.util.Random;/*/classPSOpubl

12、icdoublec1,c2;publicdoublew;publicParticlepop;publicintMaxIter;publicintnPop;publicdoublex_low;publicdoublex_up;publicdoublebest_fitness;publicdoublebest_solution;publicintcount_Iter=0;publicintVar;staticRandomrand1=newRandom();PSO(doublec1,doublec2,doublew,intVar,intMaxIter,intnPop,doublex_low,doub

13、lex_up)this.c1=c1;this.c2=c2;this.w=w;this.Var=Var;this.MaxIter=MaxIter;this.nPop=nPop;this.x_low=x_low;this.x_up=x_up;this.pop=newParticlenPop;for(inti=0;inPop;i+)popi=newParticle(Var,x_low,x_up);popi.fitness=function_fitness(popi.position);popi.Best_fitness=popi.fitness;popi.P_po=(double)popi.posi

14、tion.clone();best_fitness=pop1.fitness;best_solution=(double)pop1.position.clone();publicdoublefunction_fitness(doublevar)doublesum=0;for(inti=0;iVar;i+)sum+=vari*vari;returnsum;publicvoidup_search()for(inti=0;inPop;i+)for(intj=0;jx_up)popi.velj=x_up;if(popi.veljx_up)popi.positionj=x_up;if(popi.posi

15、tionjx_low)popi.positionj=x_low;/publicvoidup_date()for(inti=0;ithis.nPop;i+)popi.fitness=function_fitness(popi.position);if(popi.fitnesspopi.Best_fitness)popi.P_po=(double)popi.position.clone();if(popi.fitnessbest_fitness)best_fitness=popi.fitness;best_solution=(double)popi.position.clone();publicv

16、oidshow_result(intIter_c)System.out.printf(Iteration:%5dfit:%5fn,Iter_c,best_fitness);if(Iter_c=(MaxIter-1)for(inti=0;iVar;i+)System.out.println(+best_solutioni);System.out.println(ThePSOend.);publicvoidrun()up_date();for(intit=0;itMaxIter;it+)up_search();up_date();show_result(it);classParticlepubli

17、cintVar;publicdoubleposition;publicdoublevel;publicdoubleP_po;publicdoubleBest_fitness;publicdoublefitness;publicdoublex_low;publicdoublex_up;staticRandomrand1=newRandom();Particle(intVar,doublex_low,doublex_up)position=newdoubleVar;vel=newdoubleVar;P_po=newdoubleVar;this.x_low=x_low;this.x_up=x_up;

18、for(inti=0;iVar;i+)positioni=rand1.nextDouble()*(x_up-x_low)+x_low;veli=1.0*positioni;publicclassElementarypublicstaticvoidmain(Stringargs)PSOpso=newPSO(2,2,0.5,30,1000,40,-10,10);System.out.println(BEGIN);pso.run();System.out.println(END);运行结果：li?Problems孙ServersMoocTest_SConsole血由ftiGaverageElemen

19、taryJavaApplicationE:Javajdk1,8,C2C,2binjavaw.exe(2019年12月27日11;11:12)BE6IN.Iteration:0fit:949.003031Iteration:1fit:548.306943Iteration：2fit：548.306943Iteration:3fit:548.306943Iteration:4fit:528.316658Iteration:Sfit:468*208574Iterations6fit:468.208574Iteration:7fit:468.238574Iteration：8fit；460*208574Iteration:9fit:385.687869Iteration:10fit:311.383627EProblemsServeri-MochzTest|HConsoleJnJUnitwCtjyerage993994995996997990999fit：0.682481fit:.68