//  aMOCell2.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.Distance;
import jmetal.util.JMException;
import jmetal.util.Neighborhood;
import jmetal.util.Ranking;
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 the MOCell algorithm, which 
 * applies an archive feedback through parent selection. 
 */
public class aMOCell2 extends Algorithm{

  /** 
   * Constructor
   * @param problem Problem to solve
   */
  public aMOCell2(Problem problem){
    super (problem) ;
  } // aMOCell2


  /**   
   * Runs of the aMOCell2 algorithm.
   * @return a <code>SolutionSet</code> that is a set of non dominated solutions
   * as a result of the algorithm execution  
   * @throws JMException 
   */    
  public SolutionSet execute() throws JMException, ClassNotFoundException {
    //Init the param
    int populationSize, archiveSize, maxEvaluations, evaluations;
    Operator mutationOperator, crossoverOperator, selectionOperator;
    SolutionSet currentSolutionSet;
    CrowdingArchive archive;
    SolutionSet [] neighbors;    
    Neighborhood neighborhood;
    Comparator dominance = new DominanceComparator(),
    crowding  = new CrowdingComparator();  
    Distance distance = new Distance();

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

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

    //Initialize the variables
    currentSolutionSet  = new SolutionSet(populationSize);        
    archive            = new CrowdingArchive(archiveSize,problem_.getNumberOfObjectives());                
    evaluations        = 0;                        
    neighborhood       = new Neighborhood(populationSize);
    neighbors          = new SolutionSet[populationSize];


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


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

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

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

        //parents
        parents[0] = (Solution)selectionOperator.execute(neighbors[ind]);
        if (archive.size() > 0) {
          parents[1] = (Solution)selectionOperator.execute(archive);
        } else {
          parents[1] = (Solution)selectionOperator.execute(neighbors[ind]);
        }

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

        //->Evaluate solution and constraints
        problem_.evaluate(offSpring[0]);
        problem_.evaluateConstraints(offSpring[0]);
        evaluations++;

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

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

          boolean deleteMutant = true;


          int compareResult = crowding.compare(individual,offSpring[0]);
          if (compareResult == 1) { //The offSpring[0] is better
            deleteMutant = false;
          }

          if (!deleteMutant)  {
            offSpring[0].setLocation(individual.getLocation());
            currentSolutionSet.replace(offSpring[0].getLocation(),offSpring[0]);
            archive.add(new Solution(offSpring[0]));
          } else {
            archive.add(new Solution(offSpring[0]));    
          }
        }                              
      }                                                       
    }
    return archive;
  } // execute     
} // aMOCell2