粒子群算法求解优化问题(c实现)

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#include"stdio.h"#include "math.h"#include "stdlib.h"#include "time.h"#define N 100  //最大粒子数 #define D 10  //最大问题维数 double fitness(double x[],int n);typedef double (*funType)(double [],int n);double pso(funType fp,int n,double c1,double c2,double w,int m,int d,double xbest[]);int main(){double x[D];double f;f = pso(fitness,40,1.5,1.5,0.6,2000,1,x);printf("最优的x为：");for(int i=0;i<1;i++)printf("%f ",x[i]); printf("\n");printf("函数的最优值为：%f\n",f);return 0;}double fitness(double x[],int n){  //待优化函数 double f = 0;f = x[0]*x[0]-2*x[0]+1;  // f(x) = x.^2-2*x+1return f;}/*fp: 待优化的目标函数n: 粒子数目c1,c2: 学习因子1，学习因子2w: 惯性权重M: 最大迭代次数d: 问题的维数x: 返回的最优解 */double pso(funType fp,int n,double c1,double c2,double w,int m,int d,double xbest[]){int i,j,t;double x[N][D],v[N][D];double pg[D]; //全局最优解double y[N];  //粒子适应度double p[N][D];  //step1.初始化种群的个体srand((int)time(0)); for(i=0;i<n;i++)          for(j=0;j<d;j++){x[i][j] = rand()*1.0/RAND_MAX;v[i][j] = rand()*1.0/RAND_MAX;}//step2. 计算粒子适应度，初始化p和pg for(i=0;i<d;i++)pg[i] = x[n-1][i]; // pg为全局最优 for(i=0;i<n;i++){y[i] = fp(x[i],d);for(j=0;j<d;j++)p[i][j] = x[i][j];}for(i=0;i<n-1;i++){if(y[i]<y[n-1]){for(j=0;j<d;j++)pg[j] = x[i][j];}}//step3. 进入主循环，按照公式依次迭代 for(t=0;t<m;t++){         for(i=0;i<n;i++){for(j=0;j<d;j++){v[i][j] = w*v[i][j] + c1*(rand()/RAND_MAX)*(p[i][j]-x[i][j]) + c2*(rand()/RAND_MAX)*(pg[j]-x[i][j]); //更新 x[i][j] = x[i][j] + v[i][j];}if(fp(x[i],d)<y[i]){y[i] = fp(x[i],d);for(j=0;j<d;j++)p[i][j] = x[i][j];}if(y[i]<fp(pg,d)){for(j=0;j<d;j++)pg[j] = p[i][j];}} }for(i=0;i<d;i++) //最优解 xbest[i] = pg[i];  return fp(pg,d);}
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