project1.cpp

粒子群优化问题是国内比较前沿和少见的研究方向。这里有一个关于他的示例程序

//库文件
#include"stdio.h"
#include"stdlib.h"
#include"time.h"
#include"math.h"

//随机数定义
#define rdint(i) (rand()%(int)(i))
#define rdft() (float)((double)rdint(16384)/(16383.0))
#define rnd(a,b) (rdint((int)(b)-(int)(a)+1)+(int)(a))


//宏定义
#define POPSIZE 20
#define DIMENSION 2

//全局变量定义
float W=1.0;
float C1=1.8;
float C2=1.8;
float VMAX=2;
float XMIN=-2.0;
float XMAX=2.0;
float P[DIMENSION];
float PBEST;

struct indi
{
	float number[DIMENSION];
	float best[DIMENSION];
	float bestfitness;
	float fitness;
	float speed[DIMENSION];
}individual[POPSIZE];


void initiate(void);
void calculation(int number);
void globalbest(int number);
void localbest(int number);

//程序初始化定义
void initiate()
{
	int i,j;
	for(i=0;i<POPSIZE;i++)
		for(j=0;j<DIMENSION;j++)
			individual[i].number[j]=rdft()*(XMAX-XMIN)+XMIN;

	for(i=0;i<POPSIZE;i++)
		for(j=0;j<DIMENSION;j++)
			individual[i].speed[j]=VMAX*rdft();
	for(i=0;i<POPSIZE;i++)
		for(j=0;j<DIMENSION;j++)
			individual[i].best[j]=individual[i].number[j];
	for(i=0;i<POPSIZE;i++)
		calculation(i);
	for(i=0;i<POPSIZE;i++)
		individual[i].bestfitness=individual[i].fitness;
	globalbest(0);
}


//微粒历史最优位置修改程序
void localbest(int number)
{
	int i;
	if(individual[number].bestfitness>individual[number].fitness)
		for(i=0;i<DIMENSION;i++)
			individual[number].best[i]=individual[number].number[i];
	individual[number].bestfitness=individual[number].fitness;
}



//种群历史最优位置修改程序
void globalbest(int number)
{
	int i,j;
	float s=0;
	int flag=0;
	if(number==0)
	{
		s=individual[0].fitness;
		flag=0;
		for(i=1;i<POPSIZE;i++)
			if(individual[i].fitness<s)
		{
			s=individual[i].fitness;
			flag=i;
		}
	for(i=0;i<DIMENSION;i++)
		P[i]=individual[flag].number[i];
	PBEST=individual[flag].fitness;
	}
  else
  {
for(i=0;i<POPSIZE;i++)
     if(individual[i].bestfitness<PBEST)
	 {
		 for(j=0;j<DIMENSION;j++)
			 P[j]=individual[i].best[j];
		 PBEST=individual[i].bestfitness;
	 }
  }
}


//适应值函数计算程序
void calculation(int num)
{
	int i;
	float s=0.0,h=0.0;
	s=pow(individual[num].number[0]+individual[num].number[1]+1.0,
2.0)*(19.0-14.0*individual[num].number[0]+3.0*pow(individual[num].
number[0],2.0)-14.0*individual[num].number[1]+6.0*individual[num].
number[0]*individual[num].number[1]+3.0*pow(individual[num].number
[1],2.0))+1.0;
	h=30.0+pow(2.0*individual[num].number[0]-3.0*individual[num].
number[1],2.0)*(18.0-32.0*individual[num].number[0]+12.0*pow(individual[num].
number[0],2.0)+48.0*individual[num].number[1]-36.0*individual[num].number[0]
*individual[num].number[1]+27.0*pow(individual[num].number[1],2.0));
	individual[num].fitness=s*h;

}


//主程序
void main()
{
	int i,j,k,t,total=0;
	float sum=0;
	for(j=0;j<50;j++)
	{
		initiate();
		for(i=0;i<500;i++)
		{
			W=1.0-i*0.6/499;
		if((PBEST-3.0)<0.0001)
		{
			total++;
			sum=sum+i;
			break;
		}
		else
		{
			for(k=0;k<POPSIZE;k++)
			{
				for(t=0;t<DIMENSION;t++)
				{
	individual[k].speed[t]=W*individual[k].speed[t]+C1*rdft()*(individual[k].
best[t]-individual[k].number[t])+C2*rdft()*(P[t]-individual[k].number[t]);
	        if(individual[k].speed[t]>VMAX)
				individual[k].speed[t]=VMAX;
			individual[k].number[t]=individual[k].number[t]+individual[k].speed[t];
			if(individual[k].number[t]<XMIN)
				individual[k].number[t]=2*XMIN-individual[k].number[t];
			if(individual[k].number[t]>XMAX)
				individual[k].number[t]=2*XMAX-individual[k].number[t];
				}
				calculation(k);
				localbest(k);
			}
			globalbest(1);
		}
		}
		printf("%d,%f\t",i,PBEST);
	}
	printf("Total number is %d\n",total);
	printf("Sum is %f\n",sum);
	printf("Average is %f\n",sum/total);
}