f(3)函数总是计算错误
`//粒子群 PSO 算法
#include<stdio.h> #include<math.h> #include<time.h> #include<stdlib.h> #define P_num 200 //粒子数目
#define dim 50 #define low -512 //搜索域范围 #define high 512 #define iter_num 1000 #define V_max 20 //速度范围 #define c1 2 #define c2 2 #define w 0.5 #define alp 1 double particle[P_num][dim]; //个体集合 double particle_loc_best[P_num][dim]; //每个个体局部最优向量 double particle_loc_fit[P_num]; //个体的局部最优适应度,有局部最优向量计算而来 double particle_glo_best[dim]; //全局最优向量 double gfit; //全局最优适应度,有全局最优向量计算而来
double particle_v[P_num][dim]; //记录每个个体的当前代速度向量 double particle_fit[P_num]; //记录每个粒子的当前代适应度
double f1(double a[]) { int i; double sum=0.0; for(i=0; i<dim; i++) { sum+=a[i]*a[i]; } return sum; }
double f2(double a[]) { int i; double sum=0.0; for(i=0;i<dim;i++) { sum=(1+(a[i]+a[i+1]+1)(a[i]+a[i+1]+1)(19-14a[i]+3a[i]a[i]-14a[i+1]+6a[i]a[i+1]+3a[i+1]a[i+1]))(30+(2a[i]-3a[i+1])(2a[i]-3a[i+1])(18-32a[i]+12a[i]a[i]+48a[i+1]-36a[i]a[i+1]+27a[i+1]*a[i+1])); } return sum; }
double f3(double a[]) { int i; double sum=0.0; for(i=0;i<dim;i++) { sum+=-a[i]sin(sqrt(abs(a[i]))); } return sum; } double fitness(double a[]) //适应度函数 { return f2(a); } void initial() { int i,j; for(i=0; i<P_num; i++) //随即生成粒子 { for(j=0; j<dim; j++) { particle[i][j] = low+(high-low)1.0rand()/RAND_MAX; //初始化群体 particle_loc_best[i][j] = particle[i][j]; //将当前最优结果写入局部最优集合 particle_v[i][j] = -V_max+2V_max1.0rand()/RAND_MAX; //速度 } } for(i=0; i<P_num; i++) //计算每个粒子的适应度 { particle_fit[i] = fitness(particle[i]); particle_loc_fit[i] = particle_fit[i]; } gfit = particle_loc_fit[0]; //找出全局最优 j=0; for(i=1; i<P_num; i++) { if(particle_loc_fit[i]<gfit) { gfit = particle_loc_fit[i]; j = i; } } for(i=0; i<dim; i++) //更新全局最优向量 { particle_glo_best[i] = particle_loc_best[j][i]; } } void renew_particle() { int i,j; for(i=0; i<P_num; i++) //更新个体位置生成位置 { for(j=0; j<dim; j++) { particle[i][j] += alpparticle_v[i][j]; if(particle[i][j] > high) { particle[i][j] = high; } if(particle[i][j] < low) { particle[i][j] = low; } } } } void renew_var() { int i, j; for(i=0; i<P_num; i++) //计算每个粒子的适应度 { particle_fit[i] = fitness(particle[i]); if(particle_fit[i] < particle_loc_fit[i]) //更新个体局部最优值 { particle_loc_fit[i] = particle_fit[i]; for(j=0; j<dim; j++) // 更新局部最优向量 { particle_loc_best[i][j] = particle[i][j]; } } } for(i=0,j=-1; i<P_num; i++) //更新全局变量 { if(particle_loc_fit[i]<gfit) { gfit = particle_loc_fit[i]; j = i; } } if(j != -1) { for(i=0; i<dim; i++) //更新全局最优向量
{ particle_glo_best[i] = particle_loc_best[j][i]; } } for(i=0; i<P_num; i++) //更新个体速度 { for(j=0; j<dim; j++) { particle_v[i][j]=wparticle_v[i][j]+ c11.0rand()/RAND_MAX*(particle_loc_best[i][j]-particle[i][j])+ c21.0rand()/RAND_MAX*(particle_glo_best[j]-particle[i][j]); if(particle_v[i][j] > V_max) { particle_v[i][j] = V_max; } if(particle_v[i][j] < -V_max) { particle_v[i][j] = -V_max; } } } } int main() { freopen("result.txt","a+",stdout); int i=0; srand((unsigned)time(NULL)); initial(); while(i < iter_num) { renew_particle(); renew_var(); i++; } printf("粒子个数:%d\n",P_num); printf("维度为:%d\n",dim); printf("最优值为%.10lf\n", gfit); return 0; }`
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