// NSGAIIAdaptive_main.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.nsgaII;
import jmetal.core.Algorithm;
import jmetal.core.Operator;
import jmetal.core.Problem;
import jmetal.core.SolutionSet;
import jmetal.operators.selection.SelectionFactory;
import jmetal.problems.Kursawe;
import jmetal.problems.LZ09.LZ09_F3;
import jmetal.problems.ProblemFactory;
import jmetal.qualityIndicator.QualityIndicator;
import jmetal.util.Configuration;
import jmetal.util.JMException;
import jmetal.util.offspring.DifferentialEvolutionOffspring;
import jmetal.util.offspring.Offspring;
import jmetal.util.offspring.PolynomialMutationOffspring;
import jmetal.util.offspring.SBXCrossoverOffspring;
import java.io.IOException;
import java.util.HashMap;
import java.util.logging.FileHandler;
import java.util.logging.Logger;
/**
* Class implementing the NSGA-II algorithm.
* This implementation of NSGA-II makes use of a QualityIndicator object
* to obtained the convergence speed of the algorithm. This version is used
* in the paper:
* A.J. Nebro, J.J. Durillo, C.A. Coello Coello, F. Luna, E. Alba
* "A Study of Convergence Speed in Multi-Objective Metaheuristics."
* To be presented in: PPSN'08. Dortmund. September 2008.
*
* Besides the classic NSGA-II, a steady-state version (ssNSGAII) is also
* included (See: J.J. Durillo, A.J. Nebro, F. Luna and E. Alba
* "On the Effect of the Steady-State Selection Scheme in
* Multi-Objective Genetic Algorithms"
* 5th International Conference, EMO 2009, pp: 183-197.
* April 2009)
*/
public class NSGAIIAdaptive_main {
public static Logger logger_ ; // Logger object
public static FileHandler fileHandler_ ; // FileHandler object
/**
* @param args Command line arguments.
* @throws JMException
* @throws IOException
* @throws SecurityException
* Usage: three options
* - jmetal.metaheuristics.nsgaII.NSGAII_main
* - jmetal.metaheuristics.nsgaII.NSGAII_main problemName
* - jmetal.metaheuristics.nsgaII.NSGAII_main problemName paretoFrontFile
*/
public static void main(String [] args) throws
JMException,
SecurityException,
IOException,
ClassNotFoundException {
Problem problem ; // The problem to solve
Algorithm algorithm ; // The algorithm to use
Operator crossover ; // Crossover operator
Operator mutation ; // Mutation operator
Operator selection ; // Selection operator
HashMap parameters ; // Operator parameters
QualityIndicator indicators ; // Object to get quality indicators
// Logger object and file to store log messages
logger_ = Configuration.logger_ ;
fileHandler_ = new FileHandler("NSGAII_main.log");
logger_.addHandler(fileHandler_) ;
indicators = null ;
if (args.length == 1) {
Object [] params = {"Real"};
problem = (new ProblemFactory()).getProblem(args[0],params);
} // if
else if (args.length == 2) {
Object [] params = {"Real"};
problem = (new ProblemFactory()).getProblem(args[0],params);
indicators = new QualityIndicator(problem, args[1]) ;
} // if
else { // Default problem
problem = new Kursawe("Real", 3);
//problem = new Kursawe("BinaryReal", 3);
//problem = new Water("Real");
//problem = new ZDT1("ArrayReal", 100);
//problem = new ConstrEx("Real");
//problem = new DTLZ1("Real");
//problem = new OKA2("Real") ;
} // else
problem = new LZ09_F3("Real");
algorithm = new NSGAIIAdaptive(problem);
//algorithm = new ssNSGAIIAdaptive(problem);
// Algorithm parameters
algorithm.setInputParameter("populationSize",100);
algorithm.setInputParameter("maxEvaluations",150000);
// Selection Operator
parameters = null ;
selection = SelectionFactory.getSelectionOperator("BinaryTournament2", parameters) ;
// Add the operators to the algorithm
algorithm.addOperator("selection",selection);
// Add the indicator object to the algorithm
algorithm.setInputParameter("indicators", indicators) ;
Offspring[] getOffspring = new Offspring[3];
double CR, F;
getOffspring[0] = new DifferentialEvolutionOffspring(CR = 1.0, F = 0.5);
getOffspring[1] = new SBXCrossoverOffspring(1.0, 20);
//getOffspring[1] = new BLXAlphaCrossoverOffspring(1.0, 0.5);
getOffspring[2] = new PolynomialMutationOffspring(1.0/problem.getNumberOfVariables(), 20);
//getOffspring[2] = new NonUniformMutationOffspring(1.0/problem.getNumberOfVariables(), 0.5, 150000);
/*
Offspring[] getOffspring = new Offspring[2];
double CR, F;
getOffspring[0] = new SBXCrossoverOffspring(
0.9, // crossover probability
20); // distribution index for crossover
getOffspring[1] = new PolynomialOffspring(1.0/problem.getNumberOfVariables(), 20);
*/
algorithm.setInputParameter("offspringsCreators", getOffspring);
// Execute the Algorithm
long initTime = System.currentTimeMillis();
SolutionSet population = algorithm.execute();
long estimatedTime = System.currentTimeMillis() - initTime;
// Result messages
logger_.info("Total execution time: "+estimatedTime + "ms");
logger_.info("Variables values have been writen to file VAR");
population.printVariablesToFile("VAR");
logger_.info("Objectives values have been writen to file FUN");
population.printObjectivesToFile("FUN");
if (indicators != null) {
logger_.info("Quality indicators") ;
logger_.info("Hypervolume: " + indicators.getHypervolume(population)) ;
logger_.info("GD : " + indicators.getGD(population)) ;
logger_.info("IGD : " + indicators.getIGD(population)) ;
logger_.info("Spread : " + indicators.getSpread(population)) ;
logger_.info("Epsilon : " + indicators.getEpsilon(population)) ;
//int evaluations = ((Integer)algorithm.getOutputParameter("evaluations")).intValue();
//logger_.info("Speed : " + evaluations + " evaluations") ;
} // if
} //main
} // NSGAII_main