ReturnFlag CPSORSwarm::run_(){
#ifdef OFEC_CONSOLE
if(Global::msp_global->m_runId==0){
mSingleObj::getSingleObj()->setProgrOutputFlag(true);
if(mMultiModal::getPopInfor())
mMultiModal::getPopInfor()->setOutProgFlag(true);
}
#endif // OFEC_CONSOLE
createSubswarms();
ReturnFlag r_flag=Return_Normal;
while(!ifTerminating()){
for(auto &swarm:m_subPop){
if(swarm->m_popsize==0) continue;
r_flag=swarm->evolve();
if(r_flag==Return_Terminate) break;
handleReturnFlagAll(r_flag);
HANDLE_RETURN_FLAG(r_flag)
#ifdef OFEC_DEMON
vector<Algorithm*> vp;
for(auto &it:m_subPop){
vp.push_back(it.get());
}
msp_buffer->updateBuffer_(&vp);
#endif
}
if(r_flag==Return_Terminate) break;
//cout<<Global::msp_global->mp_problem->getEvaluations()<<" "<<getNumPops()<<" "<<m_subPop[findBestPop(1)]->m_best[0]->obj(0)<<endl;
measureMultiPop();
if(m_subPop.size()>1){
while(removeOverlapping()!=-1);
for(auto it=m_subPop.begin();it!=m_subPop.end();++it) (*it)->checkOverCrowd(m_subSize);
for(decltype(m_subPop.size()) i=0;i<m_subPop.size();i++){
if(m_subPop[i]->isConverged(0.0001)){
deletePopulation(i);
i--;
}
}
}
if(Global::msp_global->m_totalNumIndis<m_initialSize*m_diversityDegree){
initialize();
if(ifTerminating()) break;
createSubswarms();
}
}
return Return_Terminate;
}
bool MuSlashRhoLambdaES::startEvolutionRun( int mu,
int rho,
int lambda,
ESInitialize *init)
{
deletePopulation();
// set information struct to values
mInformation.currentGeneration = 0;
mInformation.mu = mu;
mInformation.rho = rho;
mInformation.lambda = lambda;
mInformation.numberOfLastSuccessfulIndividuals = 0;
if(mInformation.mu <= 0 || mInformation.rho <= 0 || mInformation.lambda <= 0){
return false;
}
mPopulation = init->createPopulation(mInformation.mu);
if(mPopulation.size() <= 0){
return false;
}
mInformation.numberOfStrategyParameters = mPopulation.first()->getStrategyParameters().size();
mInformation.numberOfObjectParameters = mPopulation.first()->getObjectParameters().size();
//calculate fitness values of population
bool ok = mFitnessEvaluation->doEvaluation(mPopulation,
mInformation);
if(ok == false){
return false;
}
qSort(mPopulation.begin(),
mPopulation.end(),
MuSlashRhoLambdaES::descendingOrder);
calculateStatistics();
return true;
}
MuSlashRhoLambdaES::~MuSlashRhoLambdaES()
{
deletePopulation();
}
bool MuSlashRhoLambdaES::doNextGeneration()
{
/*
QFile file ("evolution.log");
file.open(QIODevice::WriteOnly | QIODevice::Append);
QTextStream out(&file);
out << "\n\n--------------------------\nNew generation:\n";
*/
bool ok = true;
QList<ESIndividual*> offsprings;
QList<double> bestParentsFitness;
// generate offsprings
for(int i = 0; i < mInformation.lambda; i++)
{
// select parents which should be used to generate current offspring
QList<ESIndividual*> curParents = mMarriage->doMarriage(getPopulation(),
mInformation.rho,
mInformation);
// check if an error occured
if(curParents.size() <= 0){
return false;
}
// get fitness of the best parent
qSort(curParents.begin(),
curParents.end(),
MuSlashRhoLambdaES::descendingOrder);
bestParentsFitness.append(curParents.first()->getFitness());
/*
out << "\tParents:\n";
for(int parentIndex = 0; parentIndex < curParents.size(); parentIndex++)
{
out << "\t\t" << curParents.at(parentIndex)->toString() << "\n";
}
*/
// recombine offspring strategy parameters from parents
QList<OptimizationDouble> recombinedStratParas
= mStrategyParametersRecombination->doRecombination(
curParents,
ESRecombination::STRATEGY,
mInformation);
// check if an error occured
if(recombinedStratParas.size() <= 0){
return false;
}
// recombine offspring object parameters from parents
QList<OptimizationDouble> recombinedObjParas
= mObjectParametersRecombination->doRecombination(
curParents,
ESRecombination::OBJECT,
mInformation);
// check if an error occured
if(recombinedObjParas.size() <= 0){
return false;
}
// mutate the offspring strategy parameters
QList<OptimizationDouble> mutatedStratParas = mStrategyParametersMutation->doMutation(
recombinedStratParas,
mInformation);
// check if an error occured
if(mutatedStratParas.size() <= 0){
return false;
}
// mutate the offspring object parameters with help of is mutated strategy parameters
QList<OptimizationDouble> mutatedObjParas = mObjectParametersMutation->doMutation(
recombinedObjParas,
mutatedStratParas,
mInformation);
// check if an error occured
if(mutatedObjParas.size() <= 0){
return false;
}
// create new Individual and add it to offspring list
offsprings.append( new ESIndividual(mutatedObjParas, mutatedStratParas, 0.0) );
/*
out << "\t--> Offspring:\n";
out << "\t\t" << offsprings.last()->toString();
out << "\n\n";
*/
}
//calculate fitness values of offsprings
ok &= mFitnessEvaluation->doEvaluation(offsprings,
mInformation);
if(ok == false){
return false;
}
// calculate sum of offsprings which have a better fitness then their best parent
mInformation.numberOfLastSuccessfulIndividuals = 0;
for(int i = 0; i < offsprings.size(); i++)
{
if(offsprings.at(i)->getFitness() > bestParentsFitness.at(i)){
mInformation.numberOfLastSuccessfulIndividuals++;
}
}
bestParentsFitness.clear();
// sort offspring accordingly their fitness
qSort(offsprings.begin(),
offsprings.end(),
MuSlashRhoLambdaES::descendingOrder);
// get list with new population
QList<ESIndividual*> selectionList = mSelection->doSelection(mPopulation,
offsprings,
mInformation.mu,
mInformation);
// check if an error occured
if(selectionList.size() <= 0){
return false;
}
// delete old parents which are not in the selection
deletePopulation(selectionList);
// use selection as new population
mPopulation = selectionList;
// update statisticvalues in mInformation
calculateStatistics();
mInformation.currentGeneration++;
/*
file.flush();
file.close();
*/
return ok;
}
