bool PhyloMOCHC::exist(Solution & s1, SolutionSet & set2) {
for (int i = 0; i < set2.size(); i++) {
if (equalsIndividuals(s1,*set2.get(i)))
return true;
}
return false;
}
/**
* Performs the operation
* @param object Object representing a SolutionSet
* @return the selected solution
*/
void * BinaryTournament2::execute(void * object) {
SolutionSet * population = (SolutionSet *)object;
if (index_ == 0) //Create the permutation
{
PermutationUtility * permutationUtility = new PermutationUtility();
delete [] a_;
a_= permutationUtility->intPermutation(population->size());
delete permutationUtility;
}
Solution * solution1;
Solution * solution2;
solution1 = population->get(a_[index_]);
solution2 = population->get(a_[index_+1]);
index_ = (index_ + 2) % population->size();
int flag = dominance_->compare(solution1,solution2);
if (flag == -1)
return solution1;
else if (flag == 1)
return solution2;
else if (solution1->getCrowdingDistance() > solution2->getCrowdingDistance())
return solution1;
else if (solution2->getCrowdingDistance() > solution1->getCrowdingDistance())
return solution2;
else
if (PseudoRandom::randDouble()<0.5)
return solution1;
else
return solution2;
} // execute
bool PhyloMOCHC::equals(SolutionSet & set1, SolutionSet & set2) {
if (set1.size() != set2.size())
return false;
for (int i = 0; i < set1.size(); i++) {
if (!exist(*set1.get(i),set2))
return false;
}
return true;
} // returns the equal
SolutionSet *MOCHC::rankingAndCrowdingSelection(SolutionSet * pop, int size) {
SolutionSet *result = new SolutionSet(size);
// Ranking the union
Ranking * ranking = new Ranking(pop);
Distance * distance = new Distance();
int remain = size;
int index = 0;
SolutionSet * front = NULL;
// Obtain the next front
front = ranking->getSubfront(index);
while ((remain > 0) && (remain >= front->size())) {
//Assign crowding distance to individuals
distance->crowdingDistanceAssignment(front, problem_->getNumberOfObjectives());
//Add the individuals of this front
for (int k = 0; k < front->size(); k++) {
result->add(new Solution(front->get(k)));
} // for
//Decrement remain
remain = remain - front->size();
//Obtain the next front
index++;
if (remain > 0) {
front = ranking->getSubfront(index);
} // if
} // while
// Remain is less than front(index).size, insert only the best one
if (remain > 0) { // front contains individuals to insert
distance->crowdingDistanceAssignment(front, problem_->getNumberOfObjectives());
Comparator * c = new CrowdingComparator();
front->sort(c);
delete c;
for (int k = 0; k < remain; k++) {
result->add(new Solution(front->get(k)));
} // for
remain = 0;
} // if
delete ranking;
delete distance;
return result;
}
int main(int argc, char ** argv) {
clock_t t_ini, t_fin;
Problem * problem ; // The problem to solve
Algorithm * algorithm ; // The algorithm to use
//Operator * mutation ; // "Turbulence" operator
if (argc>=2) {
problem = ProblemFactory::getProblem(argc, argv);
cout << "Selected problem: " << problem->getName() << endl;
} else {
cout << "No problem selected." << endl;
cout << "Default problem will be used: Sphere" << endl;
problem = ProblemFactory::getProblem(const_cast<char *>("Sphere"));
}
algorithm = new StandardPSO2007(problem);
// Algorithm parameters
int swarmSize = 10 + (int) (2 * sqrt(problem->getNumberOfVariables()));
int maxIterations = 80000;
int numberOfParticlesToInform = 3;
algorithm->setInputParameter("swarmSize",&swarmSize);
algorithm->setInputParameter("maxIterations",&maxIterations);
algorithm->setInputParameter("numberOfParticlesToInform", &numberOfParticlesToInform);
// Execute the Algorithm
t_ini = clock();
SolutionSet * population = algorithm->execute();
t_fin = clock();
double secs = (double) (t_fin - t_ini);
secs = secs / CLOCKS_PER_SEC;
// Result messages
cout << "Total execution time: " << secs << "s" << endl;
cout << "Variables values have been written to file VAR" << endl;
population->printVariablesToFile("VAR");
cout << "Objectives values have been written to file FUN" << endl;
population->printObjectivesToFile("FUN");
delete population;
delete algorithm;
} // main
/**
* Executes the operation
* @param object An object containing the population and the position (index)
* of the current individual
* @return An object containing the three selected parents
*/
void * DifferentialEvolutionSelection::execute(void * object) {
void ** parameters = (void **)object ;
SolutionSet * population = (SolutionSet *) parameters[0];
int index = *(int *) parameters[1] ;
Solution ** parents = new Solution*[3];
int r1, r2, r3;
if (population->size() < 4) {
cerr << "DifferentialEvolutionSelection: the population has less than four solutions" << endl;
exit(-1);
}
do {
r1 = PseudoRandom::randInt(0,population->size()-1);
} while ( r1==index );
do {
r2 = PseudoRandom::randInt(0,population->size()-1);
} while ( r2==index || r2==r1 );
do {
r3 = PseudoRandom::randInt(0,population->size()-1);
} while( r3==index || r3==r1 || r3==r2 );
parents[0] = population->get(r1);
parents[1] = population->get(r2);
parents[2] = population->get(r3);
return parents ;
} // execute
bool MOCHC::equals(SolutionSet & set1, SolutionSet & set2) {
for (int i = 0; i < set1.size(); i++) {
for (int j = 0; j < set2.size(); j++) {
Solution *s1 = set1.get(i);
Solution *s2 = set2.get(j);
for (int var = 0; var < s1->getNumberOfVariables(); var++) {
Binary *b1, *b2;
b1 = (Binary *)s1->getDecisionVariables()[var];
b2 = (Binary *)s2->getDecisionVariables()[var];
for (int bit = 0; bit < b1->getNumberOfBits(); bit++) {
if (b1->getIth(bit)!=b2->getIth(bit)) {
return false;
}
}
}
}
}
return true;
} // returns the equal
/**
* Performs the operation
* @param object Object representing a SolutionSet
* @return the worst solution found
*/
void * WorstSolutionSelection::execute(void * object) {
SolutionSet * solutionSet = (SolutionSet *)object;
if (solutionSet->size() == 0) {
return NULL;
}
int worstSolution = 0;
for (int i = 1; i < solutionSet->size(); i++) {
if (comparator_->compare(solutionSet->get(i), solutionSet->get(worstSolution)) > 0) {
worstSolution = i;
}
} // for
int * intPtr = new int(worstSolution);
return intPtr;
} // execute
int main(int argc, char ** argv) {
clock_t t_ini, t_fin;
Problem * problem; // The problem to solve
Algorithm * algorithm; // The algorithm to use
Operator * crossover; // Crossover operator
Operator * mutation; // Mutation operator
//QualityIndicator * indicators ; // Object to get quality indicators
map<string, void *> parameters; // Operator parameters
//TODO: QualityIndicator * indicators; // Object to get quality indicators
if (argc>=2) {
problem = ProblemFactory::getProblem(argc, argv);
cout << "Selected problem: " << problem->getName() << endl;
} else {
cout << "No problem selected." << endl;
cout << "Default problem will be used: Kursawe" << endl;
problem = ProblemFactory::getProblem(const_cast<char *>("Kursawe"));
}
algorithm = new MOEAD(problem);
// Algorithm parameters
int populationSizeValue = 300;
int maxEvaluationsValue = 150000;
algorithm->setInputParameter("populationSize",&populationSizeValue);
algorithm->setInputParameter("maxEvaluations",&maxEvaluationsValue);
// Directory with the files containing the weight vectors used in
// Q. Zhang, W. Liu, and H Li, The Performance of a New Version of MOEA/D
// on CEC09 Unconstrained MOP Test Instances Working Report CES-491, School
// of CS & EE, University of Essex, 02/2009.
// http://dces.essex.ac.uk/staff/qzhang/MOEAcompetition/CEC09final/code/ZhangMOEADcode/moead0305.rar
string dataDirectoryValue =
"../../data/Weight";
algorithm->setInputParameter("dataDirectory", &dataDirectoryValue);
// Crossover operator
double crParameter = 1.0;
double fParameter = 0.5;
parameters["CR"] = &crParameter;
parameters["F"] = &fParameter;
crossover = new DifferentialEvolutionCrossover(parameters);
// Mutation operator
parameters.clear();
double probabilityParameter = 1.0/(problem->getNumberOfVariables());
double distributionIndexParameter = 20.0;
parameters["probability"] = &probabilityParameter;
parameters["distributionIndex"] = &distributionIndexParameter;
mutation = new PolynomialMutation(parameters);
// Add the operators to the algorithm
algorithm->addOperator("crossover",crossover);
algorithm->addOperator("mutation",mutation);
// Add the indicator object to the algorithm
//algorithm->setInputParameter("indicators", indicators) ;
// Execute the Algorithm
t_ini = clock();
SolutionSet * population = algorithm->execute();
t_fin = clock();
double secs = (double) (t_fin - t_ini);
secs = secs / CLOCKS_PER_SEC;
// Result messages
cout << "Total execution time: " << secs << "s" << endl;
cout << "Variables values have been written to file VAR" << endl;
population->printVariablesToFile("VAR");
cout << "Objectives values have been written to file FUN" << endl;
population->printObjectivesToFile("FUN");
delete mutation;
delete crossover;
delete population;
delete algorithm;
} // main
/**
* Assigns crowding distances to all solutions in a <code>SolutionSet</code>.
* @param solutionSet The <code>SolutionSet</code>.
* @param nObjs Number of objectives.
*/
void Distance::crowdingDistanceAssignment(SolutionSet * solutionSet, int nObjs) {
int size = solutionSet->size();
if (size == 0)
return;
if (size == 1) {
solutionSet->get(0)->setCrowdingDistance(std::numeric_limits<double>::max());
return;
} // if
if (size == 2) {
solutionSet->get(0)->setCrowdingDistance(std::numeric_limits<double>::max());
solutionSet->get(1)->setCrowdingDistance(std::numeric_limits<double>::max());
return;
} // if
//Use a new SolutionSet to evite alter original solutionSet
SolutionSet * front = new SolutionSet(size);
for (int i = 0; i < size; i++){
front->add(solutionSet->get(i));
}
for (int i = 0; i < size; i++)
front->get(i)->setCrowdingDistance(0.0);
double objetiveMaxn;
double objetiveMinn;
double distance;
for (int i = 0; i<nObjs; i++) {
// Sort the population by Obj n
Comparator * c = new ObjectiveComparator(i);
front->sort(c);
delete c;
objetiveMinn = front->get(0)->getObjective(i);
objetiveMaxn = front->get(front->size()-1)->getObjective(i);
//Set de crowding distance
front->get(0)->setCrowdingDistance(std::numeric_limits<double>::max());
front->get(size-1)->setCrowdingDistance(std::numeric_limits<double>::max());
for (int j = 1; j < size-1; j++) {
distance = front->get(j+1)->getObjective(i) - front->get(j-1)->getObjective(i);
distance = distance / (objetiveMaxn - objetiveMinn);
distance += front->get(j)->getCrowdingDistance();
front->get(j)->setCrowdingDistance(distance);
} // for
} // for
front->clear();
delete front;
} // crowdingDistanceAssignment
SolutionSet *MOCHC::execute() {
int populationSize;
int iterations;
int maxEvaluations;
int convergenceValue;
int minimumDistance;
int evaluations;
double preservedPopulation;
double initialConvergenceCount;
bool condition = false;
SolutionSet *solutionSet, *offSpringPopulation, *newPopulation;
Comparator * crowdingComparator = new CrowdingComparator();
SolutionSet * population;
SolutionSet * offspringPopulation;
SolutionSet * unionSolution;
Operator * cataclysmicMutation;
Operator * crossover;
Operator * parentSelection;
//Read the parameters
populationSize = *(int *) getInputParameter("populationSize");
maxEvaluations = *(int *) getInputParameter("maxEvaluations");
convergenceValue = *(int *) getInputParameter("convergenceValue");
initialConvergenceCount = *(double *)getInputParameter("initialConvergenceCount");
preservedPopulation = *(double *)getInputParameter("preservedPopulation");
//Read the operators
cataclysmicMutation = operators_["mutation"];
crossover = operators_["crossover"];
parentSelection = operators_["parentSelection"];
iterations = 0;
evaluations = 0;
// calculating the maximum problem sizes ....
Solution * sol = new Solution(problem_);
int size = 0;
for (int var = 0; var < problem_->getNumberOfVariables(); var++) {
Binary *binaryVar;
binaryVar = (Binary *)sol->getDecisionVariables()[var];
size += binaryVar->getNumberOfBits();
}
minimumDistance = (int) std::floor(initialConvergenceCount*size);
// Create the initial solutionSet
Solution * newSolution;
population = new SolutionSet(populationSize);
for (int i = 0; i < populationSize; i++) {
newSolution = new Solution(problem_);
problem_->evaluate(newSolution);
problem_->evaluateConstraints(newSolution);
evaluations++;
population->add(newSolution);
} //for
while (!condition) {
offSpringPopulation = new SolutionSet(populationSize);
Solution **parents = new Solution*[2];
for (int i = 0; i < population->size()/2; i++) {
parents[0] = (Solution *) (parentSelection->execute(population));
parents[1] = (Solution *) (parentSelection->execute(population));
if (hammingDistance(*parents[0],*parents[1])>= minimumDistance) {
Solution ** offSpring = (Solution **) (crossover->execute(parents));
problem_->evaluate(offSpring[0]);
problem_->evaluateConstraints(offSpring[0]);
problem_->evaluate(offSpring[1]);
problem_->evaluateConstraints(offSpring[1]);
evaluations+=2;
offSpringPopulation->add(offSpring[0]);
offSpringPopulation->add(offSpring[1]);
}
}
SolutionSet *join = population->join(offSpringPopulation);
delete offSpringPopulation;
newPopulation = rankingAndCrowdingSelection(join,populationSize);
delete join;
if (equals(*population,*newPopulation)) {
minimumDistance--;
}
if (minimumDistance <= -convergenceValue) {
minimumDistance = (int) (1.0/size * (1-1.0/size) * size);
int preserve = (int) std::floor(preservedPopulation*populationSize);
newPopulation->clear(); //do the new in c++ really hurts me(juanjo)
population->sort(crowdingComparator);
for (int i = 0; i < preserve; i++) {
newPopulation->add(new Solution(population->get(i)));
}
for (int i = preserve; i < populationSize; i++) {
Solution * solution = new Solution(population->get(i));
cataclysmicMutation->execute(solution);
problem_->evaluate(solution);
problem_->evaluateConstraints(solution);
newPopulation->add(solution);
}
}
iterations++;
delete population;
population = newPopulation;
if (evaluations >= maxEvaluations) {
condition = true;
}
}
return population;
}
SolutionSet *PhyloMOCHC::execute() {
int populationSize;
int iterations;
int maxEvaluations;
int convergenceValue;
int minimumDistance;
int evaluations;
int IntervalOptSubsModel;
double preservedPopulation;
double initialConvergenceCount;
bool condition = false;
SolutionSet *solutionSet, *offSpringPopulation, *newPopulation;
Comparator * crowdingComparator = new CrowdingComparator();
SolutionSet * population;
SolutionSet * offspringPopulation;
SolutionSet * unionSolution;
Operator * cataclysmicMutation;
Operator * crossover;
Operator * parentSelection;
//Read the parameters
populationSize = *(int *) getInputParameter("populationSize");
maxEvaluations = *(int *) getInputParameter("maxEvaluations");
IntervalOptSubsModel = *(int *) getInputParameter("intervalupdateparameters");
convergenceValue = *(int *) getInputParameter("convergenceValue");
initialConvergenceCount = *(double *)getInputParameter("initialConvergenceCount");
preservedPopulation = *(double *)getInputParameter("preservedPopulation");
//Read the operators
cataclysmicMutation = operators_["mutation"];
crossover = operators_["crossover"];
parentSelection = operators_["selection"];
iterations = 0;
evaluations = 0;
// calculating the maximum problem sizes ....
int size = 0;
Solution * sol = new Solution(problem_);
PhyloTree *Pt1 = (PhyloTree *)sol->getDecisionVariables()[0];
TreeTemplate<Node> * tree1 = Pt1->getTree();
BipartitionList* bipL1 = new BipartitionList(*tree1, true);
bipL1->removeTrivialBipartitions();
size = bipL1->getNumberOfBipartitions() * 2;
delete bipL1;
delete sol;
minimumDistance = (int) std::floor(initialConvergenceCount*size);
cout << "Minimun Distance " << minimumDistance << endl;
// Create the initial solutionSet
Solution * newSolution;
ApplicationTools::displayTask("Initial Population", true);
population = new SolutionSet(populationSize);
Phylogeny * p = (Phylogeny *) problem_;
for (int i = 0; i < populationSize; i++) {
newSolution = new Solution(problem_);
if(p->StartingOptRamas){
p->BranchLengthOptimization(newSolution,p->StartingMetodoOptRamas,p->StartingNumIterOptRamas,p->StartingTolerenciaOptRamas);
}
if(p->OptimizacionSubstModel){
p->OptimizarParamModeloSust(newSolution);
}
problem_->evaluate(newSolution);
problem_->evaluateConstraints(newSolution);
evaluations++;
population->add(newSolution);
} //for
ApplicationTools::displayTaskDone();
while (!condition) {
cout << "Evaluating " << evaluations << endl;
offSpringPopulation = new SolutionSet(populationSize);
Solution **parents = new Solution*[2];
for (int i = 0; i < population->size()/2; i++) {
parents[0] = (Solution *) (parentSelection->execute(population));
parents[1] = (Solution *) (parentSelection->execute(population));
if (RFDistance(parents[0],parents[1])>= minimumDistance) {
Solution ** offSpring = (Solution **) (crossover->execute(parents));
((Phylogeny *)problem_)->Optimization(offSpring[0]); //Optimize and update the scores (Evaluate OffSpring)
((Phylogeny *)problem_)->Optimization(offSpring[1]);
/*problem_->evaluate(offSpring[0]);
problem_->evaluateConstraints(offSpring[0]);
problem_->evaluate(offSpring[1]);
problem_->evaluateConstraints(offSpring[1]);*/
evaluations+=2;
offSpringPopulation->add(offSpring[0]);
offSpringPopulation->add(offSpring[1]);
delete[] offSpring;
}
}
SolutionSet *join = population->join(offSpringPopulation);
delete offSpringPopulation;
newPopulation = rankingAndCrowdingSelection(join,populationSize);
delete join;
if (equals(*population,*newPopulation)) {
minimumDistance--;
}
if (minimumDistance <= -convergenceValue) {
minimumDistance = (int) (1.0/size * (1-1.0/size) * size);
int preserve = (int) std::floor(preservedPopulation*populationSize);
newPopulation->clear();
population->sort(crowdingComparator);
for (int i = 0; i < preserve; i++) {
newPopulation->add(new Solution(population->get(i)));
}
for (int i = preserve; i < populationSize; i++) {
Solution * solution = new Solution(population->get(i));
cataclysmicMutation->execute(solution);
problem_->evaluate(solution);
problem_->evaluateConstraints(solution);
newPopulation->add(solution);
}
}
//Update Interval
if(evaluations%IntervalOptSubsModel==0 and IntervalOptSubsModel > 0){
Solution * sol; double Lk;
Phylogeny * p = (Phylogeny*) problem_;
cout << "Updating and Optimizing Parameters.." << endl;
for(int i=0; i<newPopulation->size(); i++){
sol = newPopulation->get(i);
Lk= p->BranchLengthOptimization(sol,p->OptimizationMetodoOptRamas,p->OptimizationNumIterOptRamas,p->OptimizationTolerenciaOptRamas);
sol->setObjective(1,Lk*-1);
}
cout << "Update Interval Done!!" << endl;
}
iterations++;
delete population;
population = newPopulation;
if (evaluations >= maxEvaluations) {
condition = true;
}
}
return population;
}
/*
* Runs the ssNSGA-II algorithm.
* @return a <code>SolutionSet</code> that is a set of non dominated solutions
* as a result of the algorithm execution
*/
SolutionSet * ssNSGAII::execute() {
int populationSize;
int maxEvaluations;
int evaluations;
int IntervalOptSubsModel;
// TODO: QualityIndicator indicators; // QualityIndicator object
int requiredEvaluations; // Use in the example of use of the
// indicators object (see below)
SolutionSet * population;
SolutionSet * offspringPopulation;
SolutionSet * unionSolution;
Operator * mutationOperator;
Operator * crossoverOperator;
Operator * selectionOperator;
Distance * distance = new Distance();
//Read the parameters
populationSize = *(int *) getInputParameter("populationSize");
maxEvaluations = *(int *) getInputParameter("maxEvaluations");
IntervalOptSubsModel = *(int *) getInputParameter("intervalupdateparameters");
// TODO: indicators = (QualityIndicator) getInputParameter("indicators");
//Initialize the variables
population = new SolutionSet(populationSize);
evaluations = 0;
requiredEvaluations = 0;
//Read the operators
mutationOperator = operators_["mutation"];
crossoverOperator = operators_["crossover"];
selectionOperator = operators_["selection"];
ApplicationTools::displayTask("Initial Population", true);
// Create the initial solutionSet
Solution * newSolution;
Phylogeny * p = (Phylogeny *) problem_;
for (int i = 0; i < populationSize; i++) {
newSolution = new Solution(problem_);
if(p->StartingOptRamas){
p->BranchLengthOptimization(newSolution,p->StartingMetodoOptRamas,p->StartingNumIterOptRamas,p->StartingTolerenciaOptRamas);
}
if(p->OptimizacionSubstModel)
p->OptimizarParamModeloSust(newSolution);
problem_->evaluate(newSolution);
problem_->evaluateConstraints(newSolution);
evaluations++;
population->add(newSolution);
} //for
ApplicationTools::displayTaskDone();
// Generations
while (evaluations < maxEvaluations) {
// Create the offSpring solutionSet
offspringPopulation = new SolutionSet(populationSize);
Solution ** parents = new Solution*[2];
if(evaluations%100==0){
cout << "Evaluating " << evaluations << endl;
}
//obtain parents
parents[0] = (Solution *) (selectionOperator->execute(population));
parents[1] = (Solution *) (selectionOperator->execute(population));
// crossover
Solution ** offSpring = (Solution **) (crossoverOperator->execute(parents));
// mutation
mutationOperator->execute(offSpring[0]);
((Phylogeny *)problem_)->Optimization(offSpring[0]); //Optimize and update the scores (Evaluate OffSpring)
// evaluation
//problem_->evaluate(offSpring[0]);
//problem_->evaluateConstraints(offSpring[0]);
// insert child into the offspring population
offspringPopulation->add(offSpring[0]);
evaluations ++;
delete[] offSpring;
delete[] parents;
// Create the solutionSet union of solutionSet and offSpring
unionSolution = population->join(offspringPopulation);
delete offspringPopulation;
// Ranking the union
Ranking * ranking = new Ranking(unionSolution);
int remain = populationSize;
int index = 0;
SolutionSet * front = NULL;
for (int i=0;i<population->size();i++) {
delete population->get(i);
}
population->clear();
// Obtain the next front
front = ranking->getSubfront(index);
while ((remain > 0) && (remain >= front->size())) {
//Assign crowding distance to individuals
distance->crowdingDistanceAssignment(front, problem_->getNumberOfObjectives());
//Add the individuals of this front
for (int k = 0; k < front->size(); k++) {
population->add(new Solution(front->get(k)));
} // for
//Decrement remain
remain = remain - front->size();
//Obtain the next front
index++;
if (remain > 0) {
front = ranking->getSubfront(index);
} // if
} // while
// Remain is less than front(index).size, insert only the best one
if (remain > 0) { // front contains individuals to insert
distance->crowdingDistanceAssignment(front, problem_->getNumberOfObjectives());
Comparator * c = new CrowdingComparator();
front->sort(c);
delete c;
for (int k = 0; k < remain; k++) {
population->add(new Solution(front->get(k)));
} // for
remain = 0;
} // if
delete ranking;
delete unionSolution;
//Update Interval
if(evaluations%IntervalOptSubsModel==0 and IntervalOptSubsModel > 0){
Solution * sol; double Lk;
Phylogeny * p = (Phylogeny*) problem_;
//cout << "Updating and Optimizing Parameters.." << endl;
for(int i=0; i<population->size(); i++){
sol = population->get(i);
Lk= p->BranchLengthOptimization(sol,p->OptimizationMetodoOptRamas,p->OptimizationNumIterOptRamas,p->OptimizationTolerenciaOptRamas);
sol->setObjective(1,Lk*-1);
}
//cout << "Update Interval Done!!" << endl;
}
// This piece of code shows how to use the indicator object into the code
// of NSGA-II. In particular, it finds the number of evaluations required
// by the algorithm to obtain a Pareto front with a hypervolume higher
// than the hypervolume of the true Pareto front.
// TODO:
// if ((indicators != NULL) &&
// (requiredEvaluations == 0)) {
// double HV = indicators.getHypervolume(population);
// if (HV >= (0.98 * indicators.getTrueParetoFrontHypervolume())) {
// requiredEvaluations = evaluations;
// } // if
// } // if
} // while
delete distance;
// Return as output parameter the required evaluations
// TODO:
//setOutputParameter("evaluations", requiredEvaluations);
// Return the first non-dominated front
Ranking * ranking = new Ranking(population);
SolutionSet * result = new SolutionSet(ranking->getSubfront(0)->size());
for (int i=0;i<ranking->getSubfront(0)->size();i++) {
result->add(new Solution(ranking->getSubfront(0)->get(i)));
}
delete ranking;
delete population;
return result;
} // execute
/*
* Runs the ssNSGA-II algorithm.
* @return a <code>SolutionSet</code> that is a set of non dominated solutions
* as a result of the algorithm execution
*/
SolutionSet * ssNSGAII::execute() {
int populationSize;
int maxEvaluations;
int evaluations;
// TODO: QualityIndicator indicators; // QualityIndicator object
int requiredEvaluations; // Use in the example of use of the
// indicators object (see below)
SolutionSet * population;
SolutionSet * offspringPopulation;
SolutionSet * unionSolution;
Operator * mutationOperator;
Operator * crossoverOperator;
Operator * selectionOperator;
Distance * distance = new Distance();
//Read the parameters
populationSize = *(int *) getInputParameter("populationSize");
maxEvaluations = *(int *) getInputParameter("maxEvaluations");
// TODO: indicators = (QualityIndicator) getInputParameter("indicators");
//Initialize the variables
population = new SolutionSet(populationSize);
evaluations = 0;
requiredEvaluations = 0;
//Read the operators
mutationOperator = operators_["mutation"];
crossoverOperator = operators_["crossover"];
selectionOperator = operators_["selection"];
// Create the initial solutionSet
Solution * newSolution;
for (int i = 0; i < populationSize; i++) {
newSolution = new Solution(problem_);
problem_->evaluate(newSolution);
problem_->evaluateConstraints(newSolution);
evaluations++;
population->add(newSolution);
} //for
// Generations
while (evaluations < maxEvaluations) {
// Create the offSpring solutionSet
offspringPopulation = new SolutionSet(populationSize);
Solution ** parents = new Solution*[2];
//obtain parents
parents[0] = (Solution *) (selectionOperator->execute(population));
parents[1] = (Solution *) (selectionOperator->execute(population));
// crossover
Solution ** offSpring = (Solution **) (crossoverOperator->execute(parents));
// mutation
mutationOperator->execute(offSpring[0]);
// evaluation
problem_->evaluate(offSpring[0]);
problem_->evaluateConstraints(offSpring[0]);
// insert child into the offspring population
offspringPopulation->add(offSpring[0]);
evaluations ++;
delete[] offSpring;
delete[] parents;
// Create the solutionSet union of solutionSet and offSpring
unionSolution = population->join(offspringPopulation);
delete offspringPopulation;
// Ranking the union
Ranking * ranking = new Ranking(unionSolution);
int remain = populationSize;
int index = 0;
SolutionSet * front = NULL;
for (int i=0;i<population->size();i++) {
delete population->get(i);
}
population->clear();
// Obtain the next front
front = ranking->getSubfront(index);
while ((remain > 0) && (remain >= front->size())) {
//Assign crowding distance to individuals
distance->crowdingDistanceAssignment(front, problem_->getNumberOfObjectives());
//Add the individuals of this front
for (int k = 0; k < front->size(); k++) {
population->add(new Solution(front->get(k)));
} // for
//Decrement remain
remain = remain - front->size();
//Obtain the next front
index++;
if (remain > 0) {
front = ranking->getSubfront(index);
} // if
} // while
// Remain is less than front(index).size, insert only the best one
if (remain > 0) { // front contains individuals to insert
distance->crowdingDistanceAssignment(front, problem_->getNumberOfObjectives());
Comparator * c = new CrowdingComparator();
front->sort(c);
delete c;
for (int k = 0; k < remain; k++) {
population->add(new Solution(front->get(k)));
} // for
remain = 0;
} // if
delete ranking;
delete unionSolution;
// This piece of code shows how to use the indicator object into the code
// of NSGA-II. In particular, it finds the number of evaluations required
// by the algorithm to obtain a Pareto front with a hypervolume higher
// than the hypervolume of the true Pareto front.
// TODO:
// if ((indicators != NULL) &&
// (requiredEvaluations == 0)) {
// double HV = indicators.getHypervolume(population);
// if (HV >= (0.98 * indicators.getTrueParetoFrontHypervolume())) {
// requiredEvaluations = evaluations;
// } // if
// } // if
} // while
delete distance;
// Return as output parameter the required evaluations
// TODO:
//setOutputParameter("evaluations", requiredEvaluations);
// Return the first non-dominated front
Ranking * ranking = new Ranking(population);
SolutionSet * result = new SolutionSet(ranking->getSubfront(0)->size());
for (int i=0;i<ranking->getSubfront(0)->size();i++) {
result->add(new Solution(ranking->getSubfront(0)->get(i)));
}
delete ranking;
delete population;
return result;
} // execute
/**
* Runs of the SMPSO algorithm.
* @return a <code>SolutionSet</code> that is a set of non dominated solutions
* as a result of the algorithm execution
*/
SolutionSet * PSO::execute() {
initParams();
success_ = false;
globalBest_ = NULL;
//->Step 1 (and 3) Create the initial population and evaluate
for (int i = 0; i < particlesSize_; i++) {
Solution * particle = new Solution(problem_);
problem_->evaluate(particle);
evaluations_ ++;
particles_->add(particle);
if ((globalBest_ == NULL) || (particle->getObjective(0) < globalBest_->getObjective(0))) {
if (globalBest_!= NULL) {
delete globalBest_;
}
globalBest_ = new Solution(particle);
}
}
//-> Step2. Initialize the speed_ of each particle to 0
for (int i = 0; i < particlesSize_; i++) {
speed_[i] = new double[problem_->getNumberOfVariables()];
for (int j = 0; j < problem_->getNumberOfVariables(); j++) {
speed_[i][j] = 0.0;
}
}
//-> Step 6. Initialize the memory of each particle
for (int i = 0; i < particles_->size(); i++) {
Solution * particle = new Solution(particles_->get(i));
localBest_[i] = particle;
}
//-> Step 7. Iterations ..
while (iteration_ < maxIterations_) {
int * bestIndividualPtr = (int*)findBestSolution_->execute(particles_);
int bestIndividual = *bestIndividualPtr;
delete bestIndividualPtr;
computeSpeed(iteration_, maxIterations_);
//Compute the new positions for the particles_
computeNewPositions();
//Mutate the particles_
//mopsoMutation(iteration_, maxIterations_);
//Evaluate the new particles_ in new positions
for (int i = 0; i < particles_->size(); i++) {
Solution * particle = particles_->get(i);
problem_->evaluate(particle);
evaluations_ ++;
}
//Actualize the memory of this particle
for (int i = 0; i < particles_->size(); i++) {
//int flag = comparator_.compare(particles_.get(i), localBest_[i]);
//if (flag < 0) { // the new particle is best_ than the older remember
if ((particles_->get(i)->getObjective(0) < localBest_[i]->getObjective(0))) {
Solution * particle = new Solution(particles_->get(i));
delete localBest_[i];
localBest_[i] = particle;
} // if
if ((particles_->get(i)->getObjective(0) < globalBest_->getObjective(0))) {
Solution * particle = new Solution(particles_->get(i));
delete globalBest_;
globalBest_ = particle;
} // if
}
iteration_++;
}
// Return a population with the best individual
SolutionSet * resultPopulation = new SolutionSet(1);
int * bestIndexPtr = (int *)findBestSolution_->execute(particles_);
int bestIndex = *bestIndexPtr;
delete bestIndexPtr;
cout << "Best index = " << bestIndex << endl;
Solution * s = particles_->get(bestIndex);
resultPopulation->add(new Solution(s));
// Free memory
deleteParams();
return resultPopulation;
} // execute
/**
* Runs of the SMPSO algorithm.
* @return a <code>SolutionSet</code> that is a set of non dominated solutions
* as a result of the algorithm execution
*/
SolutionSet *SMPSOhv::execute() {
initParams();
success = false;
//->Step 1 (and 3) Create the initial population and evaluate
for (int i = 0; i < swarmSize; i++){
Solution *particle = new Solution(problem_);
problem_->evaluate(particle);
problem_->evaluateConstraints(particle);
particles->add(particle);
}
//-> Step2. Initialize the speed of each particle to 0
for (int i = 0; i < swarmSize; i++) {
speed[i] = new double[problem_->getNumberOfVariables()];
for (int j = 0; j < problem_->getNumberOfVariables(); j++) {
speed[i][j] = 0.0;
}
}
// Step4 and 5
for (int i = 0; i < particles->size(); i++){
Solution *particle = new Solution(particles->get(i));
if (leaders->add(particle) == false){
delete particle;
}
}
//-> Step 6. Initialize the memory of each particle
for (int i = 0; i < particles->size(); i++){
Solution *particle = new Solution(particles->get(i));
best[i] = particle;
}
//Crowding the leaders_
//distance->crowdingDistanceAssignment(leaders, problem_->getNumberOfObjectives());
leaders->computeHVContribution();
//-> Step 7. Iterations ..
while (iteration < maxIterations) {
//Compute the speed_
computeSpeed(iteration, maxIterations);
//Compute the new positions for the particles_
computeNewPositions();
//Mutate the particles_
mopsoMutation(iteration,maxIterations);
//Evaluate the new particles in new positions
for (int i = 0; i < particles->size(); i++){
Solution *particle = particles->get(i);
problem_->evaluate(particle);
problem_->evaluateConstraints(particle);
}
//Update the archive
for (int i = 0; i < particles->size(); i++) {
Solution *particle = new Solution(particles->get(i));
if (leaders->add(particle) == false) {
delete particle;
}
}
//Update the memory of this particle
for (int i = 0; i < particles->size(); i++) {
int flag = dominance->compare(particles->get(i), best[i]);
if (flag != 1) { // the new particle is best_ than the older remembered
Solution *particle = new Solution(particles->get(i));
delete best[i];
best[i] = particle;
}
}
iteration++;
}
// Build the solution set result
SolutionSet * result = new SolutionSet(leaders->size());
for (int i=0;i<leaders->size();i++) {
result->add(new Solution(leaders->get(i)));
}
// Free memory
deleteParams();
return result;
} // execute
/*
* Runs the GDE3 algorithm.
* @return a <code>SolutionSet</code> that is a set of non dominated solutions
* as a result of the algorithm execution
*/
SolutionSet * GDE3::execute() {
int populationSize;
int maxIterations;
int evaluations;
int iterations;
SolutionSet * population;
SolutionSet * offspringPopulation;
Distance * distance;
Comparator * dominance;
Operator * crossoverOperator;
Operator * selectionOperator;
distance = new Distance();
dominance = new DominanceComparator();
Solution ** parent;
//Read the parameters
populationSize = *(int *) getInputParameter("populationSize");
maxIterations = *(int *) getInputParameter("maxIterations");
//Initialize the variables
population = new SolutionSet(populationSize);
evaluations = 0;
iterations = 0;
//Read the operators
crossoverOperator = operators_["crossover"];
selectionOperator = operators_["selection"];
// Create the initial solutionSet
Solution * newSolution;
for (int i = 0; i < populationSize; i++) {
newSolution = new Solution(problem_);
problem_->evaluate(newSolution);
problem_->evaluateConstraints(newSolution);
evaluations++;
population->add(newSolution);
} //for
// Generations ...
while (iterations < maxIterations) {
// Create the offSpring solutionSet
offspringPopulation = new SolutionSet(populationSize * 2);
for (int i = 0; i < populationSize; i++){
// Obtain parents. Two parameters are required: the population and the
// index of the current individual
void ** object1 = new void*[2];
object1[0] = population;
object1[1] = &i;
parent = (Solution **) (selectionOperator->execute(object1));
delete[] object1;
Solution * child ;
// Crossover. Two parameters are required: the current individual and the
// array of parents
void ** object2 = new void*[2];
object2[0] = population->get(i);
object2[1] = parent;
child = (Solution *) (crossoverOperator->execute(object2));
delete[] object2;
delete[] parent;
problem_->evaluate(child) ;
problem_->evaluateConstraints(child);
evaluations++ ;
// Dominance test
int result ;
result = dominance->compare(population->get(i), child) ;
if (result == -1) { // Solution i dominates child
offspringPopulation->add(new Solution(population->get(i)));
delete child;
} // if
else if (result == 1) { // child dominates
offspringPopulation->add(child) ;
} // else if
else { // the two solutions are non-dominated
offspringPopulation->add(child) ;
offspringPopulation->add(new Solution(population->get(i)));
} // else
} // for
// Ranking the offspring population
Ranking * ranking = new Ranking(offspringPopulation);
int remain = populationSize;
int index = 0;
SolutionSet * front = NULL;
for (int i = 0; i < populationSize; i++) {
delete population->get(i);
}
population->clear();
// Obtain the next front
front = ranking->getSubfront(index);
while ((remain > 0) && (remain >= front->size())){
//Assign crowding distance to individuals
distance->crowdingDistanceAssignment(front,problem_->getNumberOfObjectives());
//Add the individuals of this front
for (int k = 0; k < front->size(); k++ ) {
population->add(new Solution(front->get(k)));
} // for
//Decrement remain
remain = remain - front->size();
//Obtain the next front
index++;
if (remain > 0) {
front = ranking->getSubfront(index);
} // if
} // while
// remain is less than front(index).size, insert only the best one
if (remain > 0) { // front contains individuals to insert
while (front->size() > remain) {
distance->crowdingDistanceAssignment(front,problem_->getNumberOfObjectives());
Comparator * crowdingComparator = new CrowdingComparator();
int indexWorst = front->indexWorst(crowdingComparator);
delete crowdingComparator;
delete front->get(indexWorst);
front->remove(indexWorst);
}
for (int k = 0; k < front->size(); k++) {
population->add(new Solution(front->get(k)));
}
remain = 0;
} // if
delete ranking;
delete offspringPopulation;
iterations ++ ;
} // while
delete dominance;
delete distance;
// Return the first non-dominated front
Ranking * ranking = new Ranking(population);
SolutionSet * result = new SolutionSet(ranking->getSubfront(0)->size());
for (int i=0;i<ranking->getSubfront(0)->size();i++) {
result->add(new Solution(ranking->getSubfront(0)->get(i)));
}
delete ranking;
delete population;
return result;
} // execute
void enumerate(int limit,
int timeLimit,
int threads,
int state_tree_limit,
std::ifstream& inFile,
const std::string& intervalFile,
int lowerbound,
bool monoclonal,
const std::string& purityString,
bool writeCliqueFile,
bool readCliqueFile,
const std::string& cliqueFile,
int offset,
const IntSet& whiteList,
SolutionSet& sols)
{
CharacterMatrix M;
try
{
g_lineNumber = 0;
inFile >> M;
}
catch (std::runtime_error& e)
{
std::cerr << "Character matrix file. " << e.what() << std::endl;
exit(1);
}
if (!intervalFile.empty())
{
std::ifstream inFile2(intervalFile.c_str());
if (inFile2.good())
{
try
{
M.setIntervals(inFile2);
}
catch (std::runtime_error& e)
{
std::cerr << "Interval file. " << e.what() << std::endl;
exit(1);
}
}
}
StlDoubleVector purityValues;
if (purityString != "" && !parse_purity_vector(purityString, M.m(), purityValues))
{
return;
}
// if (monoclonal && purityString == "")
// {
// std::cerr << "Expected purity values" << std::endl;
// return;
// }
if (g_verbosity >= VERBOSE_ESSENTIAL)
{
std::cerr << "Intializing copy-state matrix ..." << std::endl;
}
M.init();
if (monoclonal)
{
M.applyHeuristic();
}
CompatibilityGraph* pComp = NULL;
if (!readCliqueFile)
{
if (g_verbosity >= VERBOSE_ESSENTIAL)
{
std::cerr << std::endl << "Initializing compatibility graph ..." << std::endl;
}
pComp = new CompatibilityGraph(M);
pComp->init(IntPairSet(), -1);
}
else
{
std::ifstream inFile(cliqueFile);
pComp = new CompatibilityGraph(M);
pComp->init(inFile);
}
if (writeCliqueFile)
{
std::ofstream outFile(cliqueFile);
if (outFile.good())
{
pComp->write(outFile);
}
}
NoisyCnaEnumerate alg(M, purityValues, *pComp, lowerbound);
alg.init(state_tree_limit);
alg.enumerate(limit, timeLimit, threads, state_tree_limit, monoclonal, offset, whiteList);
sols = alg.sols();
delete pComp;
if (g_verbosity >= VERBOSE_ESSENTIAL)
{
std::cerr << "Generated " << sols.solutionCount() << " solutions" << std::endl;
}
inFile.close();
}
int main(int argc, char ** argv) {
clock_t t_ini, t_fin;
Problem * problem ; // The problem to solve
Algorithm * algorithm ; // The algorithm to use
Operator * crossover ; // Crossover operator
Operator * selection ; // Selection operator
map<string, void *> parameters;
//TODO: QualityIndicator * indicators;
if (argc>=2) {
problem = ProblemFactory::getProblem(argc, argv);
cout << "Selected problem: " << problem->getName() << endl;
} else {
cout << "No problem selected." << endl;
cout << "Default problem will be used: Fonseca" << endl;
problem = ProblemFactory::getProblem(const_cast<char *>("Fonseca"));
}
algorithm = new GDE3(problem);
// Algorithm parameters
int populationSizeValue = 100;
int maxIterationsValue = 250;
algorithm->setInputParameter("populationSize",&populationSizeValue);
algorithm->setInputParameter("maxIterations",&maxIterationsValue);
// Crossover operator
double crParameter = 0.5;
double fParameter = 0.5;
parameters["CR"] = &crParameter;
parameters["F"] = &fParameter;
crossover = new DifferentialEvolutionCrossover(parameters);
// Selection operator
parameters.clear();
selection = new DifferentialEvolutionSelection(parameters) ;
// Add the operators to the algorithm
algorithm->addOperator("crossover",crossover);
algorithm->addOperator("selection",selection);
// Add the indicator object to the algorithm
//algorithm->setInputParameter("indicators", indicators) ;
// Execute the Algorithm
t_ini = clock();
SolutionSet * population = algorithm->execute();
t_fin = clock();
double secs = (double) (t_fin - t_ini);
secs = secs / CLOCKS_PER_SEC;
// Result messages
cout << "Total execution time: " << secs << "s" << endl;
cout << "Variables values have been written to file VAR" << endl;
population->printVariablesToFile("VAR");
cout << "Objectives values have been written to file FUN" << endl;
population->printObjectivesToFile("FUN");
delete selection;
delete crossover;
delete population;
delete algorithm;
} // main
/**
* Class implementing the NSGA-II algorithm.
* This implementation of NSGA-II makes use of a QualityIndicator object
* to obtained the convergence speed of the algorithm. This version is used
* in the paper:
* A.J. Nebro, J.J. Durillo, C.A. Coello Coello, F. Luna, E. Alba
* "A Study of Convergence Speed in Multi-Objective Metaheuristics."
* To be presented in: PPSN'08. Dortmund. September 2008.
*
* Besides the classic NSGA-II, a steady-state version (ssNSGAII) is also
* included (See: J.J. Durillo, A.J. Nebro, F. Luna and E. Alba
* "On the Effect of the Steady-State Selection Scheme in
* Multi-Objective Genetic Algorithms"
* 5th International Conference, EMO 2009, pp: 183-197.
* April 2009)
*/
int main(int argc, char ** argv) {
clock_t t_ini, t_fin;
Problem * problem ; // The problem to solve
Algorithm * algorithm ; // The algorithm to use
HUXCrossover * crossover ; // Crossover operator
BitFlipMutation * mutation ; // Mutation operator
BinaryTournament * selection ; // Selection operator
if (argc>=2) {
problem = ProblemFactory::getProblem(argc, argv);
cout << "Selected problem: " << problem->getName() << endl;
} else {
cout << "No problem selected." << endl;
cout << "Default problem will be used: Fonseca" << endl;
problem = ProblemFactory::getProblem(const_cast<char *>("ZDT5"));
}
// QualityIndicator * indicators ; // Object to get quality indicators
// indicators = NULL ;
algorithm = new MOCHC(problem);
// Algorithm parameters
int populationSize = 100;
int maxEvaluations = 25000;
double initialConvergenceCount = 0.25;
double preservedPopulation = 0.05;
int convergenceValue = 3;
algorithm->setInputParameter("populationSize",&populationSize);
algorithm->setInputParameter("maxEvaluations",&maxEvaluations);
algorithm->setInputParameter("initialConvergenceCount",&initialConvergenceCount);
algorithm->setInputParameter("preservedPopulation",&preservedPopulation);
algorithm->setInputParameter("convergenceValue",&convergenceValue);
// Mutation and Crossover for Real codification
map<string, void *> parameters;
double crossoverProbability = 1.0;
double crossoverDistributionIndex = 20.0;
parameters["probability"] = &crossoverProbability;
crossover = new HUXCrossover(parameters);
parameters.clear();
double mutationProbability = 0.35;
parameters["probability"] = &mutationProbability;
mutation = new BitFlipMutation(parameters);
// Selection Operator
parameters.clear();
selection = new BinaryTournament(parameters);
// Add the operators to the algorithm
algorithm->addOperator("crossover",crossover);
algorithm->addOperator("mutation",mutation);
algorithm->addOperator("parentSelection",selection);
// Add the indicator object to the algorithm
// algorithm->setInputParameter("indicators", indicators) ;
// Execute the Algorithm
t_ini = clock();
SolutionSet * population = algorithm->execute();
t_fin = clock();
double secs = (double) (t_fin - t_ini);
secs = secs / CLOCKS_PER_SEC;
// Result messages
cout << "Total execution time: " << secs << "s" << endl;
cout << "Variables values have been written to file VAR" << endl;
population->printVariablesToFile("VAR");
cout << "Objectives values have been written to file FUN" << endl;
population->printObjectivesToFile("FUN");
delete selection;
delete mutation;
delete crossover;
delete population;
delete algorithm;
} // main
int main(int argc, char ** argv) {
clock_t t_ini, t_fin;
Problem * problem; // The problem to solve
Algorithm * algorithm; // The algorithm to use
Operator * mutation; // "Turbulence" operator
map<string, void *> parameters; // Operator parameters
//TODO: QualityIndicator * indicators; // Object to get quality indicators
if (argc>=2) {
problem = ProblemFactory::getProblem(argc, argv);
cout << "Selected problem: " << problem->getName() << endl;
} else {
cout << "No problem selected." << endl;
cout << "Default problem will be used: Kursawe" << endl;
problem = ProblemFactory::getProblem(const_cast<char *>("Kursawe"));
}
algorithm = new SMPSO(problem);
// Algorithm parameters
int swarmSizeValue = 100;
int archiveSizeValue = 100;
int maxIterationsValue = 250;
algorithm->setInputParameter("swarmSize",&swarmSizeValue);
algorithm->setInputParameter("archiveSize",&archiveSizeValue);
algorithm->setInputParameter("maxIterations",&maxIterationsValue);
// Mutation operator
double probabilityParameter = 1.0/(problem->getNumberOfVariables());
double distributionIndexParameter = 20.0;
parameters["probability"] = &probabilityParameter;
parameters["distributionIndex"] = &distributionIndexParameter;
mutation = new PolynomialMutation(parameters);
// Add the operators to the algorithm
algorithm->addOperator("mutation",mutation);
// Add the indicator object to the algorithm
//algorithm->setInputParameter("indicators", indicators) ;
// Execute the Algorithm
t_ini = clock();
SolutionSet * population = algorithm->execute();
t_fin = clock();
double secs = (double) (t_fin - t_ini);
secs = secs / CLOCKS_PER_SEC;
// Result messages
cout << "Total execution time: " << secs << "s" << endl;
cout << "Variables values have been written to file VAR" << endl;
population->printVariablesToFile("VAR");
cout << "Objectives values have been written to file FUN" << endl;
population->printObjectivesToFile("FUN");
delete mutation;
delete population;
delete algorithm;
} // main
