// AbYSS_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.abyss;
import jmetal.core.Algorithm;
import jmetal.core.Operator;
import jmetal.core.Problem;
import jmetal.core.SolutionSet;
import jmetal.operators.crossover.CrossoverFactory;
import jmetal.operators.localSearch.MutationLocalSearch;
import jmetal.operators.mutation.MutationFactory;
import jmetal.problems.ProblemFactory;
import jmetal.problems.ZDT.ZDT4;
import jmetal.qualityIndicator.QualityIndicator;
import jmetal.util.Configuration;
import jmetal.util.JMException;
import java.io.IOException;
import java.util.HashMap;
import java.util.logging.FileHandler;
import java.util.logging.Logger;
/**
* This class is the main program used to configure and run AbYSS, a
* multiobjective scatter search metaheuristics, which is described in:
* A.J. Nebro, F. Luna, E. Alba, B. Dorronsoro, J.J. Durillo, A. Beham
* "AbYSS: Adapting Scatter Search to Multiobjective Optimization."
* IEEE Transactions on Evolutionary Computation. Vol. 12,
* No. 4 (August 2008), pp. 439-457
*/
public class AbYSS_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 choices
* - 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 improvement ; // Operator for improvement
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("AbYSS.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 ZDT4("ArrayReal", 10);
//problem = new ConstrEx("Real");
//problem = new DTLZ1("Real");
//problem = new OKA2("Real") ;
} // else
// STEP 2. Select the algorithm (AbYSS)
algorithm = new AbYSS(problem) ;
// STEP 3. Set the input parameters required by the metaheuristic
algorithm.setInputParameter("populationSize", 20);
algorithm.setInputParameter("refSet1Size" , 10);
algorithm.setInputParameter("refSet2Size" , 10);
algorithm.setInputParameter("archiveSize" , 100);
algorithm.setInputParameter("maxEvaluations", 25000);
// STEP 4. Specify and configure the crossover operator, used in the
// solution combination method of the scatter search
parameters = new HashMap() ;
parameters.put("probability", 0.9) ;
parameters.put("distributionIndex", 20.0) ;
crossover = CrossoverFactory.getCrossoverOperator("SBXCrossover", parameters);
// STEP 5. Specify and configure the improvement method. We use by default
// a polynomial mutation in this method.
parameters = new HashMap() ;
parameters.put("probability", 1.0/problem.getNumberOfVariables()) ;
parameters.put("distributionIndex", 20.0) ;
mutation = MutationFactory.getMutationOperator("PolynomialMutation", parameters);
parameters = new HashMap() ;
parameters.put("improvementRounds", 1) ;
parameters.put("problem",problem) ;
parameters.put("mutation",mutation) ;
improvement = new MutationLocalSearch(parameters);
// STEP 6. Add the operators to the algorithm
algorithm.addOperator("crossover",crossover);
algorithm.addOperator("improvement",improvement);
long initTime ;
long estimatedTime ;
initTime = System.currentTimeMillis();
// STEP 7. Run the algorithm
SolutionSet population = algorithm.execute();
estimatedTime = System.currentTimeMillis() - initTime;
// STEP 8. Print the results
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)) ;
} // if
} //main
} // AbYSS_main