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; }
/* * 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 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