fastpga_main.java

这是多目标进化算法包

/**
 * Main.java
 *
 * @author Juanjo Durillo
 * @version 1.0
 */
package jmetal.metaheuristics.fastPGA;

import jmetal.base.*;
import jmetal.base.operator.comparator.FPGAFitnessComparator;
import jmetal.base.operator.crossover.*   ;
import jmetal.base.operator.mutation.*    ; 
import jmetal.base.operator.selection.*   ;
import jmetal.base.variable.*             ;
import jmetal.metaheuristics.fastPGA.FastPGA;
import jmetal.problems.*                  ;
import jmetal.problems.ZDT.*              ;
import jmetal.problems.WFG.*              ;
import jmetal.problems.DTLZ.*             ;
import jmetal.util.JMException;

public class FastPGA_main {

  /**
   * @param args Command line arguments. The first (optional) argument specifies 
   *             the problem to solve.
   * @throws JMException 
   */
  public static void main(String [] args) throws JMException {
    Problem   problem   ;         // The problem to solve
    Algorithm algorithm ;         // The algorithm to use
    Operator  crossover ;         // Crossover operator
    Operator  mutation  ;         // Mutation operator
    Operator  selection ;         // Selection operator

    if (args.length == 1) {
      Object [] params = {"Real"};
      problem = (new ProblemFactory()).getProblem(args[0],params);
    } // if
    else { // Default problem
      problem = new Kursawe(3, "Real"); 
      //problem = new Kursawe(3,"BinaryReal");
      //problem = new Water("Real");
      //problem = new ZDT4("Real");
      //problem = new WFG1("Real");
      //problem = new DTLZ1("Real");
      //problem = new OKA2("Real") ;
    } // else

    algorithm = new FastPGA(problem);

    algorithm.setInputParameter("maxPopSize",100);
    algorithm.setInputParameter("initialPopulationSize",100);
    algorithm.setInputParameter("maxEvaluations",25000);
    algorithm.setInputParameter("a",20.0);
    algorithm.setInputParameter("b",1.0);
    algorithm.setInputParameter("c",20.0);
    algorithm.setInputParameter("d",0.0);

    // Parameter "termination"
    // If the preferred stopping criterium is PPR based, termination must 
    // be set to 0; otherwise, if the algorithm is intended to iterate until 
    // a give number of evaluations is carried out, termination must be set to 
    // that number
    algorithm.setInputParameter("termination",1);

    // Mutation and Crossover for Real codification 
    crossover = CrossoverFactory.getCrossoverOperator("SBXCrossover");                   
    crossover.setParameter("probability",1.0);                   
    crossover.setParameter("distributionIndex",20.0);


    mutation = MutationFactory.getMutationOperator("PolynomialMutation");                    
    mutation.setParameter("probability",1.0/problem.getNumberOfVariables());
    mutation.setParameter("distributionIndex",20.0);


    // Mutation and Crossover for Binary codification
    /*
    crossover = CrossoverFactory.getCrossoverOperator("SinglePointCrossover");                   
    crossover.setParameter("probability",1.0);  
    mutation = MutationFactory.getMutationOperator("BitFlipMutation");                    
    mutation.setParameter("probability",1.0/149.0);    
     */

    selection = new BinaryTournament(new FPGAFitnessComparator());  

    algorithm.addOperator("crossover",crossover);
    algorithm.addOperator("mutation",mutation);
    algorithm.addOperator("selection",selection);

    long initTime = System.currentTimeMillis();
    SolutionSet population = algorithm.execute();
    long estimatedTime = System.currentTimeMillis() - initTime;
    System.out.println("Total execution time: "+estimatedTime);

    System.out.println("Total number of evaluations: " + 
        algorithm.getOutputParameter("evaluations"));

    System.out.println("Objectives values have been writen to file FUN");
    population.printObjectivesToFile("FUN");
    System.out.println("Variables values have been writen to file VAR");
    population.printVariablesToFile("VAR");          
  }//main
}