C++ SolutionSet类代码示例

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++) {
//.........这里部分代码省略.........

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];
	
//.........这里部分代码省略.........

/*
 * 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]);
//.........这里部分代码省略.........

/*
 * 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());
//.........这里部分代码省略.........

/**
 * 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