C语言达成基本PSO算法

发布时间：2021-11-22 11:53:34 所属栏目：教程来源：互联网

导读：粒子群算法的流程图如上，看了好多版本，这个最靠谱，我的main函数完全按照这个来，好理解过程： int main(int argc, constchar *argv[]) { int n=0; //printf(Random Initialization of the swarm:nn); RandInitofSwarm(); //printf(Computation of the f

粒子群算法的流程图如上，看了好多版本，这个最靠谱，我的main函数完全按照这个来，好理解过程：

int main(int argc, constchar *argv[])
{
int n=0;
//printf("Random Initialization of the swarm:nn");
RandInitofSwarm();
//printf("Computation of the fitness of each particle:n");
ComputFitofSwarm();
//printf("FirstComputPandGbest:n");
FirstComputPandGbest();
while(n++!=N)
{
printf("The %dth time to calculate .n",n );
//printf("Updated of the swarm:nn");
UpdateofVandX();
//printf("Updated of the swarm's Fitness:n");
ComputFitofSwarm();
//printf("Replaced of P and Gbest:nn");
UpdatePandGbest();
}
getchar();
return 0;
}
各种printf用来调试自己的信息，可以去掉看看中间值～～～

先来pso.h

现在执行最简单的功能，用粒子群搜索二维空间：y=x^2+y^+3的最大值～～～x,y的范围属于（-100,100）,那最大值就是20003,伪随机粒子数20个，最大迭代次数为40次，最大搜索速度为2,惯性权重W设为1.4，学习因子c1=c2=2，看能不能找到最大值～～～

#ifndef _PSO_H_
#define _PSO_H_
#define Dim 2
#define PNum 20
#define N 40
typedefstruct PARTICLE{
double X[Dim];
double P[Dim];
double V[Dim];
double Fitness;
}particle;
typedefstruct SWARM{
particle Particle[PNum];
int GBestIndex;
double GBest[Dim];
double W;
double C1;
double C2;
double Xup[Dim];
double Xdown[Dim];
double Vmax[Dim];
}swarm;
void RandInitofSwarm(void);
void ComputFitofSwarm(void);
void FirstComputPandGbest(void);
void UpdateofVandX(void);
void UpdatePandGbest(void);
double InertWeight(void);
int CriteriaofStop(void);
#endif
pso.c

#include "stdio.h"
#include "stdlib.h"
#include "time.h"
#include "math.h"
#include "pso.h"
#include "stdio.h"
swarm *s=(swarm *)malloc(sizeof(swarm));
particle *p=(particle *)malloc(sizeof(particle));
int main(int argc, constchar *argv[])
{
int n=0;
//printf("Random Initialization of the swarm:nn");
RandInitofSwarm();
//printf("Computation of the fitness of each particle:n");
ComputFitofSwarm();
//printf("FirstComputPandGbest:n");
FirstComputPandGbest();
while(n++!=N)
{
printf("The %dth time to calculate .n",n );
//printf("Updated of the swarm:nn");
UpdateofVandX();
//printf("Updated of the swarm's Fitness:n");
ComputFitofSwarm();
//printf("Replaced of P and Gbest:nn");
UpdatePandGbest();
}
getchar();
return 0;
}
//Random Initialization of the swarm
void RandInitofSwarm(void)
{
int i,j;
s->W=1.4;
s->C1=2.0;
s->C2=2.0;
for(j=0;j<Dim;j++)
{
s->Xdown[j] = -100;
s->Xup[j] = 100;
s->Vmax[j] = 2;
}
srand((unsigned)time(NULL));
for(i=0; i<PNum; i++)
{
//printf(" The %dth of X is: ",i);
for(j=0; j<Dim; j++)
{
s->Particle[i].X[j] = rand()/(double)RAND_MAX*(s->Xup[j]-s->Xdown[j])+s->Xdown[j]; //-100~100
s->Particle[i].V[j] = rand()/(double)RAND_MAX*s->Vmax[j]*2-s->Vmax[j]; //-2~2
//printf(" %.2f n ",s->Particle[i].X[j]);
}
}
}
//Computation of the fitness of each particle
void ComputFitofSwarm(void)
{
int i;
srand((unsigned)time(NULL));
for(i=0; i<PNum; i++)
{
//printf(" The Fitness of %dth Particle: ",i);
s->Particle[i].Fitness = s->Particle[i].X[0]*s->Particle[i].X[0]+s->Particle[i].X[1]*s->Particle[i].X[1]+3;
//printf(" %.2fn",s->Particle[i].Fitness);
}
}
void FirstComputPandGbest(void)
{
int i,j;
//P=X;
for(i=0; i<PNum; i++)
{
for(j=0;j<Dim;j++)
{
s->Particle[i].P[j]=s->Particle[i].X[j];
}
}
//Computation of GBest
s->GBestIndex = 0;
for(i=0; i<PNum; i++)
if(s->Particle[i].Fitness>=s->Particle[s->GBestIndex].Fitness)
s->GBestIndex = i;
for(j=0;j<Dim;j++)
{
s->GBest[j]=s->Particle[s->GBestIndex].P[j];
}
printf("GBestIndex , GBest , Fitness of GBest:%d ,%.2f ,%.2f ,%.2f n",
s->GBestIndex ,s->GBest[0],s->GBest[1],
s->Particle[s->GBestIndex].Fitness);
}
//update V and X
void UpdateofVandX(void)
{
int i,j;
srand((unsigned)time(NULL));
for(i=0; i<PNum; i++)
{
//printf(" The %dth of X is: ",i);
for(j=0; j<Dim; j++)
s->Particle[i].V[j] = s->W*s->Particle[i].V[j]+
rand()/(double)RAND_MAX*s->C1*(s->Particle[i].P[j] - s->Particle[i].X[j])+
rand()/(double)RAND_MAX*s->C2*(s->GBest[j] - s->Particle[i].X[j]);
for(j=0; j<Dim; j++)
{
if(s->Particle[i].V[j]>s->Vmax[j])
s->Particle[i].V[j] = s->Vmax[j];
if(s->Particle[i].V[j]<-s->Vmax[j])
s->Particle[i].V[j] = -s->Vmax[j];
}
for(j=0; j<Dim; j++)
{
s->Particle[i].X[j] += s->Particle[i].V[j];
if(s->Particle[i].X[j]>s->Xup[j])
s->Particle[i].X[j]=s->Xup[j];
if(s->Particle[i].X[j]<s->Xdown[j])
s->Particle[i].X[j]=s->Xdown[j];
}
//printf(" %.2f %.2f n",s->Particle[i].X[0],s->Particle[i].X[1]);
}
}
staticdouble ComputAFitness(double X[])
{
return X[0]*X[0]+X[1]*X[1]+3;
}
void UpdatePandGbest(void)
{
int i,j;
//update of P if the X is bigger than current P
for (i = 0; i < PNum; i++)
{
//printf(" The %dth of P is: ",i);
if (s->Particle[i].Fitness > ComputAFitness(s->Particle[i].P))
{
for(j=0;j<Dim;j++)
{
s->Particle[i].P[j] = s->Particle[i].X[j];
}
}
//printf(" %.2f %.2f n",s->Particle[i].P[0],s->Particle[i].P[1]);
}
for (i = 0; i < PNum; i++)
{
//printf("The %dth of P's Fitness is : %.2f n",i,ComputAFitness(s->Particle[i].P));
}
//update of GBest
for(i=0; i<PNum; i++)
if(ComputAFitness(s->Particle[i].P) >= s->Particle[s->GBestIndex].Fitness)
s->GBestIndex = i;
for(j=0;j<Dim;j++)
{
s->GBest[j]=s->Particle[s->GBestIndex].P[j];
}
printf("GBestIndex , GBest , Fitness of GBest:%d ,%.2f ,%.2f ,%.2f n",
s->GBestIndex ,s->GBest[0],s->GBest[1],
ComputAFitness(s->GBest));
}
double InertWeight(void)
{
return 1.0;
}
int CriteriaofStop()
{
int n=N;
return (n--==0);
}

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