//  aMOCell3.java
//
//  Author:
//       Antonio J. Nebro <antonio@lcc.uma.es>
//       Juan J. Durillo <durillo@lcc.uma.es>
//
//  Copyright (c) 2011 Antonio J. Nebro, Juan J. Durillo
//
//  This program is free software: you can redistribute it and/or modify
//  it under the terms of the GNU Lesser General Public License as published by
//  the Free Software Foundation, either version 3 of the License, or
//  (at your option) any later version.
//
//  This program is distributed in the hope that it will be useful,
//  but WITHOUT ANY WARRANTY; without even the implied warranty of
//  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
//  GNU Lesser General Public License for more details.
// 
//  You should have received a copy of the GNU Lesser General Public License
//  along with this program.  If not, see <http://www.gnu.org/licenses/>.

package jmetal.metaheuristics.mocell;

import jmetal.core.*;
import jmetal.util.*;
import jmetal.util.archive.CrowdingArchive;
import jmetal.util.comparators.CrowdingComparator;
import jmetal.util.comparators.DominanceComparator;

import java.util.Comparator;

/**
 * This class represents an asynchronous version of MOCell algorithm. It is
 * based on aMOCell1 but replacing the worst neighbor. 
 */
public class aMOCell3 extends Algorithm{

  public aMOCell3(Problem problem){
    super (problem) ;
  }

  /** Execute the algorithm 
   * @throws jmetal.util.JMException */
  public SolutionSet execute() throws JMException, ClassNotFoundException {
    int populationSize, archiveSize, maxEvaluations, evaluations, feedBack;
    Operator mutationOperator, crossoverOperator, selectionOperator;
    SolutionSet currentPopulation;
    CrowdingArchive archive;
    SolutionSet [] neighbors;    
    Neighborhood neighborhood;
    Comparator dominance = new DominanceComparator(); 
    Comparator crowdingComparator = new CrowdingComparator();
    Distance distance = new Distance();

    //Read the parameters
    populationSize    = ((Integer)getInputParameter("populationSize")).intValue();
    archiveSize       = ((Integer)getInputParameter("archiveSize")).intValue();
    maxEvaluations    = ((Integer)getInputParameter("maxEvaluations")).intValue();                
    feedBack          = ((Integer)getInputParameter("feedBack")).intValue();


    //Read the operators
    mutationOperator  = operators_.get("mutation");
    crossoverOperator = operators_.get("crossover");
    selectionOperator = operators_.get("selection");        

    //Initialize the variables
    //Initialize the population and the archive
    currentPopulation  = new SolutionSet(populationSize);        
    archive            = new CrowdingArchive(archiveSize,problem_.getNumberOfObjectives());                
    evaluations        = 0;                        
    neighborhood       = new Neighborhood(populationSize);
    neighbors          = new SolutionSet[populationSize];
    
    //Create the comparator for check dominance
    dominance = new DominanceComparator();

    //Create the initial population
    for (int i = 0; i < populationSize; i++){
      Solution individual = new Solution(problem_);
      problem_.evaluate(individual);           
      problem_.evaluateConstraints(individual);
      currentPopulation.add(individual);
      individual.setLocation(i);
      evaluations++;
    }          

    while (evaluations < maxEvaluations){                                 
      for (int ind = 0; ind < currentPopulation.size(); ind++) {
        Solution individual = new Solution(currentPopulation.get(ind));

        Solution [] parents = new Solution[2];
        Solution [] offSpring;

        //neighbors[ind] = neighborhood.getFourNeighbors(currentPopulation,ind);
        neighbors[ind] = neighborhood.getEightNeighbors(currentPopulation,ind);                                                           
        neighbors[ind].add(individual);

        //parents
        parents[0] = (Solution)selectionOperator.execute(neighbors[ind]);
        parents[1] = (Solution)selectionOperator.execute(neighbors[ind]);

        //Create a new individual, using genetic operators mutation and crossover
        offSpring = (Solution [])crossoverOperator.execute(parents);               
        mutationOperator.execute(offSpring[0]);

        //->Evaluate individual an his constraints
        problem_.evaluate(offSpring[0]);
        problem_.evaluateConstraints(offSpring[0]);
        evaluations++;
        //<-Individual evaluated

        int flag = dominance.compare(individual,offSpring[0]);

        if (flag == 1){ //The new individuals dominate
          offSpring[0].setLocation(individual.getLocation());                                      
          currentPopulation.replace(offSpring[0].getLocation(),offSpring[0]);
          archive.add(new Solution(offSpring[0]));                   
        } else if (flag == 0) {//The individuals are non-dominates
          neighbors[ind].add(offSpring[0]);               
          offSpring[0].setLocation(-1);
          Ranking rank = new Ranking(neighbors[ind]);
          for (int j = 0; j < rank.getNumberOfSubfronts(); j++) {
            distance.crowdingDistanceAssignment(rank.getSubfront(j),problem_.getNumberOfObjectives());
          }
          Solution worst = neighbors[ind].worst(crowdingComparator);

          if (worst.getLocation() == -1) {//The worst is the offspring
            archive.add(new Solution(offSpring[0]));
          } else {
            offSpring[0].setLocation(worst.getLocation());
            currentPopulation.replace(offSpring[0].getLocation(),offSpring[0]);
            archive.add(new Solution(offSpring[0]));
          }                                          
        }
      }

      //Store a portion of the archive into the population
      distance.crowdingDistanceAssignment(archive,problem_.getNumberOfObjectives());                      
      for (int j = 0; j < feedBack; j++){
        if (archive.size() > j){
          int r = PseudoRandom.randInt(0,currentPopulation.size()-1);
          if (r < currentPopulation.size()){
            Solution individual = archive.get(j);
            individual.setLocation(r);
            currentPopulation.replace(r,new Solution(individual));
          }
        }
      }                      
    }
    
    return archive;
  }        
} // aMOCell3

