粒子群算法(PSO)

Agent() // 初始化
{
srand( (unsigned)(time( NULL )+rand()) );
int i = 0;
for(; i<iAgentDim; i++) {
dpos[i] = rnd(iRangL,iRangR);
dv[i] = dpbest[i] = dpos[i];
// PSO.cpp
//粒子群优化算法基本程序
#include <iomanip>
#include <iostream>
#define M_PI 3.14159265359
#define M_E 2.71828182845
#include <math.h>
#include <ctime>
const double delta2 = 1.494;
#define rnd(low,uper) ((rand()/(double)RAND_MAX)*((uper)-(low))+(low))
double gbest[iAgentDim]; // global best fitness保留全局最优值的坐标
}
// The Rastrigin function 测试函数！！！！！
// int i=0;
// for (; i<iAgentDim; i++) {
// sum1 += (dpos[i]*dpos[i])-3.0*cos(2*M_PI*dpos [i]);
// }
// m_dFitness = 3.0*iAgentDim + sum1;
// 找到一个更好的值后，更新 m_dBestfitness
if (m_dFitness < m_dBestfitness) {
m_dBestfitness = m_dFitness;
int i = 0;
for(; i<iAgentDim; i++) {
dpbest[i] = dpos[i];
using namesnt // 这个类表示单个的粒子
{
public:
double dpos[iAgentDim]; // 位置，也就是各个维数的值
double dpbest[iAgentDim]; // 维护一个"自己"找到的最优值的解
}
}
if (m_iTempPos != 999)
{
int j;
for(j=0; j<iAgentDim; j++) {
gbest[j] = agents[m_iTempPos].dpos[j]; // 记录全局最优解的各个坐标
}
}
cout << setiosflags(ios::fixed) << setprecision(9) << m_dBestFitness << endl;
int m_iTempPos;
public:
void Init(); // 初始化，
void Search();
};
void PSO::Search()
{
int k=0;
FILE *fp1;
if((fp1=fopen("PSO.txt","w")) == NULL) {
agents[i].UpdateFitness();
}
}
int main(int argc, char* argv[]) {
PSO pso;
pso.Init ();
pso.Search();
cout << endl;
// system("pause");
return 0;
fprintf(fp1,"%f\n",m_dBestFitness);
// 下一次迭代
for(i=0; i<iPSONum; i++) {
agents[i].UpdatePos();
agents[i].UpdateFitness ();
}
k++;
printf("Cannot open the file(%s)!\n");
return;
}
while( k < iStep ) {
m_iTempPos = 999;
int i;
for(i=0; i<iPSONum; i++) { // 此处是找鸟群中有没有更好的解，如果有，记录下来
double dv[iAgentDim]; // 速度
double m_dFitness; // agent's fitness 当前算出的一个值
double m_dBestfitness; //agent's best fitness 自己已经找到的最好值
}
}
void UpdateFitness()
// calculate the fitness and find out the best fitness,record
{
double sum1 = 0;
double sum2 = 0;
// Ackley Funtion 测试函数！！！
int iStep = 500; // 迭代次数
// 下面的值，要具体程序中具体的修改，根据你优化的函数来修改
double w = 0.8; // 惯性系数
const double delta1 = 1.494; // 加速度
fprintf(fp1,"The best result is: %2.11f after %d step. \n",m_dBestFitness,k);
for (int i=0; i<iAgentDim; i++) {
if(i%3 == 0) {
fprintf(fp1,"\n");
}
cout << "The best result is: " << setiosflags(ios::fixed) << setprecision(11)
<< m_dBestFitness << " after " << k << " step. " << endl;
dpos[i] += dv[i];
}
}
};
class PSO // 这是粒子群
{
private:
Agent agents[iPSONum];
double m_dBestFitness; // 鸟群找到的最优值
for (int i=0; i<iAgentDim; i++) {
sum1 += (dpos [i]*dpos [i]);
sum2 += cos(2*M_PI*dpos [i]);
}
m_dFitness = (-20*exp(-0.2*(sqrt((1.0/(double)iAgentDim *sum1))))-exp((1.0/(double)iAgentDim )*sum2)+20+M_E);
}
void PSO::Init() // 初始化
{
int i=0;
m_dBestFitness = 100000;
srand( (unsigned)(time( NULL )+rand()) );
for(; i<iPSONum; i++) {
agents[i].m_dBestfitness = 100000;// 将 m_dBestfitness 赋值为一个大的值，目的是找最小值，
if (m_dBestFitness > agents[i].m_dBestfitness )
{
m_dBestFitness = agents[i].m_dBestfitness;
m_iTempPos = i; // 找到到的最好解的位置
}
}
}
void UpdatePos() // agent moving
{
int i=0;
for(; i<iAgentDim; i++) {
dv[i] = w*dv[i]+delta1*rnd(0,1)*(dpbest[i]-dpos[i])+delta2*rnd(0,1)*(gbest[i]-dpos[i]);
cout << endl;
}
cout << setiosflags(ios::fixed) << setprecision(11) << " " << gbest[i];
fprintf(fp1,"%2.11f ",gbest[i]);
}
fclose(fp1);
const int iAgentDim = 3; // 优化函数的维数
const double iRangL = -50; // 函数的取值范围