C++ SolutionSet::add方法代码示例

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;	
}

/**
 * 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 *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

/**
 * 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

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.........

/*
 * 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++) {
//.........这里部分代码省略.........