半变异函数拟合指数模型

半边也函数的应用半变异函数拟合指数模型程序(c++代码)#include <stdio.h>#include <time.h>#include<windows.h>#include <math.h>#include <stdlib.h>#define S 1 /*试验次数*/#define G 2000 /*混合迭代次数*/#define P 200 /*个体总数*/#define M 20 /*族群数*/#define I 10 /*因此，一个族群中的个体数是10*/#define V 3 /*个体维数*/#define N 10 /*族群内更新次数*/#define MAX 10#define MIN 0double DMAX=1.0; /*蛙跳的最大值*/double DMIN=0.4; /*蛙跳的最大值*/double D=MAX/1; /*蛙跳的最大值*/int i1,i2,i3,i4,ii;int try_number=0;int try_max=5;double C=1.0;#define R ((double)(rand()%10000)/10000)//0-1之间的随机数,精度为1/10000 //#define R1 rand()%100/100.0static int kk;double PI=3.14159265;double Tolerance=0.0000001;//收敛精度double c3=0.03;//扰动幅度double e=2.718281828459;//自然对数底数int sm=3;int bz=0;//扰动因子标志double aw[V];double nihe[17][2]={1115.658026,8.70628355,1915.362904,8.20840555,2467.305693,9.1856689,2952.330784,9.0543057,5095.207855,9.132906445,5418.830566,8.852431395,4146.89209,9.45153145,6037.806376,9.103558859,4818.459044,7.2313171,5143.558017,9.0538129,5459.844361,9.74985695,5762.570046,8.6310193,6060.453719,9.194387,6356.051127,10.398948,6651.015103,9.8449629,6941.254523,7.2279982,7223.868903,6.579128};typedef struct {double d[V];double fitness;}Individal;typedef struct {double h[V];}heli;Individal pw[M];/*族群中个体最差位置*/Individal pb[M];/*族群中个体最好位置*/Individal px;/*全体中最好位置*/Individal individual[P];/*全部个体*/Individal pop[M][I];/*排序后的群组*/Individal temp[M];Individal temp1[I];Individal tem;Individal temx[S];/*计算标准差*//*选择测试函数为Sphere*/double fitness(double a[]){int i;double sum=0.0;double sum1=0.0;double s1=0.0,h1=0.0;double x1[V+1];for(i=0;i<V;i++)x1[i]=a[i];for(i=0;i<V;i++)for(i=0;i<17;i++){if(nihe[i][0]>x1[2])sum1=x1[0]+x1[1];elsesum1=nihe[i][1]-(x1[0]+x1[1]*(1.5*nihe[i][0]-0.5*pow(nihe[i][0],3)/pow(x1[2],3)));sum=sum+sum1;}return sum;}/*对每一个个体初始化*/void init(){int i,j;srand((unsigned)time(NULL)+kk++);for(i=0;i<P;i++){for(j=0;j<V;j++){individual[i].d[j]=R*(MAX-MIN)+MIN;}individual[i].fitness=fitness(individual[i].d);px.fitness=individual[P-1].fitness;}}/*按照适应度降序对全部个体进行排序和族群划分*/void sort(){int i,j,k;for(i=1;i<P;i++){for(j=0;j<P-i;j++){if(individual[j].fitness<individual[j+1].fitness){tem=individual[j];individual[j]=individual[j+1];individual[j+1]=tem;}}}k=0;/*按照规则分组*/for(i=0;i<I;i++){for(j=0;j<M;j++){pop[j][i]=individual[k];k++;}}if (px.fitness>individual[P-1].fitness)px=individual[P-1];for(i=0;i<M;i++){pw[i]=pop[i][0];pb[i]=pop[i][I-1];}}/*对某个群组中的个体进行重新排序*/void sortPop(int b){int i,j;for(i=1;i<I;i++){for(j=0;j<I-i;j++){if(pop[b][j].fitness<pop[b][j+1].fitness){tem=pop[b][j];pop[b][j]=pop[b][j+1];pop[b][j+1]=tem;}}}}/*群组内更新*/void update(){int i,j,k,l,n;double a;double b;for(n=0;n<N;n++){for(i=0;i<M;i++){// temp1[I]=pop[i][];a=0.0;b=0.0;// fitnessFw(i);//D=DMIN+(DMAX-DMIN)*(G-i2)/G;//D=DMIN+(DMAX-DMIN)*pow(e,-30*pow(i2/G,sm));for(j=0;j<V;j++){// temp[i].d[j]=R*(pb[i].d[j]-pw[i].d[j])+aw[j];temp[i].d[j]=C*R*(pb[i].d[j]-pw[i].d[j]);if(fabs(temp[i].d[j])>D){if(temp[i].d[j]>0){temp[i].d[j]=D;}else{temp[i].d[j]=-D;}}temp[i].d[j]+=pw[i].d[j];if(temp[i].d[j]>MAX)temp[i].d[j]=MAX;if(temp[i].d[j]<MIN)temp[i].d[j]=MIN;}a=fitness(temp[i].d);temp[i].fitness=a;if(a<pw[i].fitness){pop[i][0]=temp[i];sortPop(i);pw[i]=pop[i][0];pb[i]=pop[i][I-1];}else//标志{for(k=0;k<V;k++){// temp[i].d[k]=R*(px.d[k]-pw[i].d[k])+aw[k];temp[i].d[k]=C*R*(px.d[k]-pw[i].d[k]);if(fabs(temp[i].d[k])>D){if(temp[i].d[k]>0.0){temp[i].d[k]=D;}else{temp[i].d[k]=-D;}}temp[i].d[k]+=pw[i].d[k];if(temp[i].d[k]>MAX)temp[i].d[k]=MAX;if(temp[i].d[k]<MIN)temp[i].d[k]=MIN;// a+=temp[i].d[k]*temp[i].d[k];//适应度值计算//z=z+(x1[i]*x1[i]-10*cos(2*PI*x1[i])+10);}a=fitness(temp[i].d);temp[i].fitness=a;if(a<pw[i].fitness){pop[i][0]=temp[i];sortPop(i);pw[i]=pop[i][0];pb[i]=pop[i][I-1];}else{for(l=0;l<V;l++){pop[i][0].d[l]=R*(MAX-MIN)+MIN;// b+=pop[i][0].d[l]*pop[i][0].d[l];//适应度值计算//pop[i][0].fitness=b;}pop[i][0].fitness=fitness(pop[i][0].d);sortPop(i);pw[i]=pop[i][0];pb[i]=pop[i][I-1];}}//标志}//****M循环for(ii=0;ii<M;ii++)if(px.fitness>pb[ii].fitness)px=pb[ii];}//***N循环}/*将pop[M][I]复制到individual*/void copy(){int i,j,k;i=0;for(j=0;j<M;j++){for(k=0;k<I;k++){individual[i]=pop[j][k];i++;}}}void report(){printf("第%d次试验极值为%.10e\n",i1+1,px.fitness);}double sigma(){int j;double f=0.0;double fitness_avg=0.0;for (j=0;j<S;j++){// printf("极值e为%16f\n",temx[j].fitness);fitness_avg=fitness_avg+temx[j].fitness;}fitness_avg=fitness_avg/S;printf("平均值为%.16e\n",fitness_avg);//printf("%d极值e为%.16f\n",j,temx[j].fitness);for (j=0;j<S;j++)f=f+fabs(temx[j].fitness-fitness_avg)*fabs(temx[j].fitness-fitness_avg);// printf("极值e为%.16f\n",f);f=sqrt(f/(S-1));return f;}void main(){clock_t start,end;double ave,sigmax;FILE *f=fopen("result(SFLA).txt","w");ave=0.0;start=clock();for(i1=0;i1<S;i1++){init();for(i2=0;i2<G;i2++){sort();update();copy();}temx[i1]=px;report();ave=ave+px.fitness;}sigmax=sigma();end=clock();ave=ave/S;//printf("平均极值为\n%.16f\nCompleted!",ave);printf("50次试验标准差为%.16e\n",sigmax);printf("50次试验平均运行时间=%.2fseconds\n",(double)(end-start)/(S*(double)CLOCKS_PER_SEC));printf("50次试验的平均极值为%.16e\n",ave);fprintf(f,"50次试验平均极值为%.16f\n",ave);fprintf(f,"50次试验标准差为%.16f\n",sigmax);fprintf(f,"50次试验平均运行时间=%.2fseconds\n",(double)(end-start)/(S*(double)CLOCKS_PER_SEC)); fprintf(f,"解为：%.3f %.3f %.3f \n",px.d[0],px.d[1],px.d[2]);。