?? efile.h
字號(hào):
#include <stdio.h>#include <stdlib.h>#include <iostream>#include <vector>#include <math.h>using namespace std;#define DIMENSIONS 30#define OBJS 3#define NPARTICLES 40#define MAXGBESTS 200class EPSs{public: vector<double> eps;};class Particle{public: vector<double> vel; vector<double> x; vector<double> nx; vector<double> fx; int subswarm; vector<double> xpbest; vector<double> fxpbest; vector<EPSs> epss; Particle::Particle(){ vel.resize(DIMENSIONS); fx.resize(OBJS); fxpbest.resize(OBJS); x.resize(DIMENSIONS); nx.resize(DIMENSIONS); xpbest.resize(DIMENSIONS); } Particle::Particle(int _dimensions, int _objectives){ vel.resize(_dimensions); fx.resize(_objectives); fxpbest.resize(_objectives); x.resize(_dimensions); nx.resize(_dimensions); xpbest.resize(_dimensions); }};class EFILE{ vector<double> tlb,tub; int ndimensions; int nobjectives; int nsolutions; int nclusters; int allnsolutions; int maxsolutions; bool hasentered; bool firsttime; vector<double> xtlb,xtub; vector<int> nsolutionsbyswarm; bool hasclusteredfirsttime; int pmsize; double proximitymatrix[MAXGBESTS][MAXGBESTS]; int proximitymatrixindex[800][800];public: bool updatematrix; vector<double> EPS; vector<Particle> solutions; vector<Particle> allsolutions; void init(int _ndimensions, int _nobjectives, int _nclusters,int _maxsolutions); EFILE(); int nClusteredSolutions(int _whichcluster); int selectClusteredSolution(int _whichcluster,int _whichsolution); // void add(vector<Particle> &_sol); //void add(Particle _s); void output(); bool add(Particle &_s,int _whichcluster=-1); bool addAll(Particle &_s); void update(); bool reAdd(); void finalSolutions(); void deleteExcedent(); int falseReAdd(); void printSolutions(); int domine(vector<double> &_a,vector<double> &_b); int domine1(vector<double> &_a,vector<double> &_b); int domine2(vector<double> &_a,vector<double> &_b); double euclideanDistance(vector<double> &_a,vector<double> &_b); void normalizeDistances1(); void normalizeDistances2(); int deleteFromCluster(int _whichsolution); void add2Cluster(int _whichcluster); int substractFromTo(int _from, int _to, int _quantity); int updateClusters(vector<int> &_whichparticles); void normalizeDistances(); void hierarchicalClustering(); void createProximityMatrix(); void copy2Column(int _i); void copy2Row(int _i); void findLowestValue(int *_i,int *_j); void deleteRow(int _j); void deleteColumn(int _i); void joinColumns(int _i,int _j); int selectRandomSolution(int _whichcluster); int selectClusteredRandomSolution(int _whichcluster); double rnd(double _min,double _max); int nSolutions();};class MOPSO{ vector<double> lb,ub; int ndimensions; int nobjectives; int nparticles; int gmax; vector<Particle> particles; double W,C1,C2; int nclusters; EFILE archive; int gen;public: MOPSO(int _dims, int _objs,int _parts,int _nclusters,int _gmax,double *_lb,double *_ub ); ~MOPSO(); double rnd(double _min,double _max); void function(int _w); void initialize(); int selectGBest(int _swarm); void flight(); void execute(); void copyx(vector<double> &_a,vector<double> &_b); void copyfx(vector<double> &_a,vector<double> &_b); void copy(vector<double> &_a,vector<double> &_b); void addClusters(); int selectGbest(int _whichcluster); void perturbation(int );};/********************************************************************************************/void EFILE::normalizeDistances(){ for(int _i(0);_i<ndimensions;_i++) { xtlb[_i]=10*pow(10.0,10.0); xtub[_i]=-10*pow(10.0,10.0); } for(int _i(0);_i<nclusters;_i++){ nsolutionsbyswarm[_i]=0; } for(int _j(0);_j<nsolutions;_j++){ for(int _i(0);_i<ndimensions;_i++) { if(solutions[_j].x[_i] < xtlb[_i]) xtlb[_i]=solutions[_j].x[_i]; if(solutions[_j].x[_i] > xtub[_i]) xtub[_i]=solutions[_j].x[_i]; } } for(int _j(0);_j<nsolutions;_j++){ for(int _i(0);_i<ndimensions;_i++) { solutions[_j].nx[_i]=(double)(solutions[_j].x[_i]-xtlb[_i]); if(abs(xtub[_i]-xtlb[_i])==0)solutions[_j].nx[_i]=0; else solutions[_j].nx[_i]/=(double)(xtub[_i]-xtlb[_i]); } }}void EFILE::hierarchicalClustering(){ int _i,_j; if(nsolutions>nclusters+1){ hasclusteredfirsttime=true; updatematrix=false; normalizeDistances(); createProximityMatrix(); while(pmsize>nclusters){ findLowestValue(&_i,&_j); copy2Column(_i); copy2Column(_j); joinColumns(_i,_j); copy2Row(_i); deleteColumn(_j); deleteRow(_j); pmsize--; } for(_i=0;_i<nclusters;_i++){ for(_j=0;proximitymatrixindex[_i][_j]!=-1;_j++); /* { for(int _k=0;_k<ndimensions;_k++){ gbests4swarm[_i][_j].x[_k]=solutions[proximitymatrixindex[_i][_j]].x[_k]; } for(int _k=0;_k<nobjectives;_k++){ gbests4swarm[_i][_j].fx[_k]=solutions[proximitymatrixindex[_i][_j]].fx[_k]; } }*/ nsolutionsbyswarm[_i]=_j; } }}void EFILE::createProximityMatrix(){ int _i(0),_j(0); for(int _k(0);_k<nsolutions-1;_k++,_i++){ proximitymatrix[_i][_i]=0; _j=_i+1; for(int _l(_k+1);_l<nsolutions;_l++,_j++){ proximitymatrix[_i][_j]=euclideanDistance(solutions[_k].nx,solutions[_l].nx); } pmsize=_i+1; for(int _i(0);_i<pmsize;_i++){ proximitymatrixindex[_i][0]=_i; for(int _j(1);_j<pmsize;_j++){ proximitymatrixindex[_i][_j]=-1; } } }}void EFILE::copy2Column(int _i){ for(int _k(_i+1);_k<pmsize;_k++){ proximitymatrix[_k][_i]=proximitymatrix[_i][_k]; }}void EFILE::copy2Row(int _i){ for(int _k(_i+1);_k<pmsize;_k++){ proximitymatrix[_i][_k]=proximitymatrix[_k][_i]; }}void EFILE::findLowestValue(int *_i,int *_j){ double _maxvalue=10*pow(10.0,6.0); int _tempi=-1,_tempj=-1; for(int _k(0);_k<pmsize;_k++){ for(int _l=_k+1;_l<pmsize;_l++){ if(proximitymatrix[_k][_l]<_maxvalue){ _tempi=_k; _tempj=_l; _maxvalue=proximitymatrix[_k][_l]; } } } if(_tempi>_tempj) { int _tmp=_tempi; _tempi=_tempj; _tempj=_tmp; } *_i=_tempi; *_j=_tempj;}void EFILE::deleteRow(int _j){ for(int _k(_j);_k+1<pmsize;_k++){ for(int _l(0);_l<pmsize;_l++){//ojo hay que optimizar proximitymatrix[_k][_l]=proximitymatrix[_k+1][_l]; } int _m(0); for(;proximitymatrixindex[_k+1][_m]!=-1;_m++) proximitymatrixindex[_k][_m]=proximitymatrixindex[_k+1][_m]; proximitymatrixindex[_k][_m]=-1; }}void EFILE::deleteColumn(int _i){ for(int _k(_i);_k+1<pmsize;_k++){ for(int _l(0);_l<pmsize;_l++){//ojo hay que optimizar proximitymatrix[_l][_k]=proximitymatrix[_l][_k+1]; } }}void EFILE::joinColumns(int _i,int _j){ for(int _k=0;_k < pmsize;_k++) { double _ii=proximitymatrix[_k][_i]; double _jj=proximitymatrix[_k][_j]; proximitymatrix[_k][_i]=(_ii<_jj)?_ii:_jj;//si se toma el m醩 cercano } int _l(0); for(;proximitymatrixindex[_i][_l]!=-1;_l++); int _k(0); for(;proximitymatrixindex[_j][_k]!=-1;_k++){ proximitymatrixindex[_i][_l+_k]=proximitymatrixindex[_j][_k]; } proximitymatrixindex[_i][_l+_k]=-1;}/********************************************************************************************//*Algorithm.- Begin.- Hacer.- Crear la matriz de eps..- Buscar el valor de eps m醩 chico..- Eliminar el renglon y la columna del valor m醩 pequenio..- Buscar actualizar el nuevo valor m醩 pequenio para la dimensi髇 eliminada..- Mientras el valor de ndimensions sea alcanzado..- End. */EFILE::EFILE(){ nsolutions=0; hasentered=false; firsttime=true; updatematrix=true;}void EFILE::init(int _ndimensions, int _nobjectives, int _nclusters, int _maxsolutions){ nobjectives=_nobjectives; ndimensions=_ndimensions; nclusters=_nclusters; maxsolutions=_maxsolutions; updatematrix=true; hasclusteredfirsttime=false; EPS.resize(_nobjectives); tlb.resize(_nobjectives); tub.resize(_nobjectives); xtlb.resize(_ndimensions); xtub.resize(_ndimensions); nsolutions=0; for(int _i(0);_i<nobjectives;_i++){ EPS[_i]=0.01; } for(int _i(0);_i<nobjectives;_i++) { tlb[_i]=10*pow(10.0,10.0); tub[_i]=-10*pow(10.0,10.0); } for(int _i(0);_i<ndimensions;_i++) { xtlb[_i]=10*pow(10.0,10.0); xtub[_i]=-10*pow(10.0,10.0); } nsolutionsbyswarm.resize(nclusters); for(int _i(0);_i<nclusters;_i++){ nsolutionsbyswarm[_i]=0; } hasentered=false; firsttime=true;}void EFILE::update(){ if(firsttime){ normalizeDistances1(); firsttime=false; } else normalizeDistances2(); hasentered=true;}void EFILE::printSolutions(){ for(int _i(0);_i<nsolutions;_i++){ for(int _j(0);_j<nobjectives;_j++) cout<<solutions[_i].fx[_j]<<" "; cout<<endl; }}//// Funcion que indica si un individuo domina a otro// si el primero domina al segundo retorna 1// si el segundo domina al primero retoruna -1// si son iguales retorna 0// si ninguno domina retorna 11//int EFILE::domine1(vector<double> &_a,vector<double> &_b){ int anterior = 0, mejor; for(int i=0;i<nobjectives;i++){ if(_a[i] <_b[i]) mejor = 1; else if(_b[i]<_a[i])mejor = -1; else mejor = 0; if(mejor!=anterior&&anterior!=0&&mejor!=0) return(11); if(mejor!=0) anterior = mejor; } return(anterior);}int EFILE::domine(vector<double> &_a,vector<double> &_b){ int _tmp; if(!hasentered){ _tmp=domine1(_a,_b); } else _tmp=domine2(_a,_b); return _tmp;} bool EFILE::add(Particle &_particle,int _whichcluster){ bool _flag=true; vector<bool> erase; vector<int> _delparticles; addAll(_particle); for(int _i(0);_i<nsolutions;_i++){ int _tmp=domine(solutions[_i].fx,_particle.fx); if(_tmp==1||_tmp==0) { _flag=false; } if(_tmp==-1){?? 快捷鍵說明
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