参考文献：
量子行为粒子群优化算法研究[D], 孙俊
#include <iostream> #include <math.h> #include <time.h> using namespace std; #define M 50 //群体数目50 #define N 4 //每个粒子的维数4 #define NN 500 //整体迭代次数 #define chaotic_count 3 //判断是否进入停滞状态 #define gama 0.001 #define R 0.8 #define chaotic_counts 100 //混沌搜索的迭代次数 //测试类 class TestFunction { public: double resen(double x1,double x2,double x3,double x4) { double s=0; s=100*(x2-x1*x1)*(x2-x1*x1)+(1-x1)*(1-x1)+s; s=100*(x3-x2*x2)*(x3-x2*x2)+(1-x2)*(1-x2)+s; s=100*(x4-x3*x3)*(x4-x3*x3)+(1-x3)*(1-x3)+s; return s; } }; class CQPSO { private: double delta; double (*w)[N];// = new double[50][4]; //总体粒子 double *f;//=new double[M];//适应度值 double *ff;//=new double[M];//相对f的比较值 double (*p)[N];//=new double[M][N]; double *g;//=new double[N]; double *c;//=new double[N]; int flag;//=0; TestFunction *tf;// = new TestFunction; double random() { double s; s=(abs(rand())%10000+10000)/10000.0-1.0; return s; } public: CQPSO(double delta) { int i,j; this->delta=delta; w=new double[M][N]; f=new double[M]; ff=new double[M]; p=new double[M][N]; g=new double[N]; c=new double[N]; tf=new TestFunction; flag=0; for(i=0;i<M;i++) { for(j=0;j<N;j++) { w[i][j]=random(); } } } void CQPSOmethod(int count) { int i,j; bool b; if(count==1) { for(i=0;i<M;i++) { for(j=0;j<N;j++) { p[i][j]=w[i][j]; } f[i]=tf->resen(w[i][0],w[i][1],w[i][2],w[i][3]); } cqpso_p();//f);//得出全局最优 } if(count>1) { cqpso_update( );//w ); for(i=0;i<M;i++) { ff[i]=tf->resen(w[i][0],w[i][1],w[i][2],w[i][3]); if(ff[i]<f[i]) { f[i]=ff[i]; for(j=0;j<N;j++) p[i][j]=w[i][j]; } } cqpso_p();//f); b=chaotic_whether( ); if(b==true) flag=flag+1; else flag=0; if(flag==chaotic_count) { chaotic(); flag=0; } } cout<<(tf->resen(g[0],g[1],g[2],g[3]))<<endl; } void cqpso_p()//double *f)//得到个体最优中最小值——全局最优 { double temp=f[0]; int i,j; for(i=1;i<M;i++) { if(f[i]<temp) { temp=f[i]; } } for(i=0;i<M;i++) { if(temp==f[i]) { for(j=0;j<N;j++) { g[j]=p[i][j]; } break; } } } void cqpso_c()//double (*p)[N]) { int i,j; for(i=0;i<N;i++) c[i]=0; for(i=0;i<N;i++) { for(j=0;j<M;j++) { c[i]=c[i]+p[j][i]; } } for(i=0;i<N;i++) c[i]=c[i]/M; } void cqpso_update( )//double (*w)[N] ) { int i,j; double *fai=new double[N]; double (*u)[N]=new double[M][N]; double (*pp)[N]=new double[M][N]; for(i=0;i<N;i++) { fai[i]=random(); } for(i=0;i<M;i++) { for(j=0;j<N;j++) u[i][j]=random(); } cqpso_c( );//p ); for(i=0;i<M;i++) { for(j=0;j<N;j++) pp[i][j]=fai[j]*p[i][j]+g[j]*(1-fai[j]); } for(i=0;i<M;i++) { for(j=0;j<N;j++) w[i][j]=pp[i][j]+delta*(abs(c[j]-w[i][j]))*log(1/u[i][j]); } } //混沌搜索核心算法 void chaotic() { int i,j; double *y=new double[N]; double *yy=new double[N]; double *yyy=new double[N]; double f_chaotic;//*f_chaotic=new double[chaotic_counts]; double ff_chaotic; for(i=0;i<N;i++) { y[i]=random(); } for(j=1;j<chaotic_counts;j++) { if(j==1) { for(i=0;i<N;i++) { yy[i]=g[i]+R*(2*y[i]-1); } f_chaotic=tf->resen(yy[0],yy[1],yy[2],yy[3]); for(i=0;i<N;i++) { yyy[i]=y[i]; } } if(j>1) { for(i=0;i<N;i++) { y[i]=4*y[i]*(1-y[i]); } for(i=0;i<N;i++) { yy[i]=g[i]+R*(2*y[i]-1); } ff_chaotic=tf->resen(yy[0],yy[1],yy[2],yy[3]); if(ff_chaotic<f_chaotic) { f_chaotic=ff_chaotic; for(i=0;i<N;i++) { yyy[i]=y[i]; } } } } if(f_chaotic<(tf->resen(g[0],g[1],g[2],g[3]))) { for(i=0;i<N;i++) { g[i]=yyy[i]; } } } //判断是否进入混沌状态 bool chaotic_whether( ) { double Fbest; Fbest=tf->resen(g[0],g[1],g[2],g[3]); double temp=ff[0]; int i;//,j; for(i=1;i<M;i++) { if(ff[i]<temp) { temp=ff[i]; } } if(((temp-Fbest)/temp)<gama) return true; else return false; } }; int main() { int i; srand((unsigned)time(0)); CQPSO *qo = new CQPSO(0.5); //qo->w=new double[M][N]; for(i=1;i<NN;i++) qo->CQPSOmethod(i); }