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package org.uma.jmetal.experiment;
import org.uma.jmetal.algorithm.Algorithm;
import org.uma.jmetal.algorithm.multiobjective.nsgaii.NSGAIIBuilder;
import org.uma.jmetal.algorithm.multiobjective.smpso.SMPSOBuilder;
import org.uma.jmetal.algorithm.multiobjective.spea2.SPEA2Builder;
import org.uma.jmetal.operator.impl.crossover.SBXCrossover;
import org.uma.jmetal.operator.impl.mutation.PolynomialMutation;
import org.uma.jmetal.problem.DoubleProblem;
import org.uma.jmetal.problem.Problem;
import org.uma.jmetal.problem.multiobjective.zdt.ZDT1;
import org.uma.jmetal.qualityindicator.impl.*;
import org.uma.jmetal.qualityindicator.impl.hypervolume.PISAHypervolume;
import org.uma.jmetal.solution.DoubleSolution;
import org.uma.jmetal.util.JMetalException;
import org.uma.jmetal.util.archive.impl.CrowdingDistanceArchive;
import org.uma.jmetal.util.evaluator.impl.SequentialSolutionListEvaluator;
import org.uma.jmetal.util.experiment.Experiment;
import org.uma.jmetal.util.experiment.ExperimentBuilder;
import org.uma.jmetal.util.experiment.component.*;
import org.uma.jmetal.util.experiment.util.ExperimentAlgorithm;
import org.uma.jmetal.util.experiment.util.ExperimentProblem;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
/**
* Example of experimental study based on solving the ZDT1 problem but using five different number
* of variables. This can be interesting to study the behaviour of the algorithms when solving an
* scalable problem (in the number of variables). The used algorithms are NSGA-II, SPEA2 and SMPSO.
*
* This experiment assumes that the reference Pareto front is of problem ZDT1 is not known, so a
* reference front must be obtained.
*
* Six quality indicators are used for performance assessment.
*
* The steps to carry out the experiment are: 1. Configure the experiment 2. Execute the algorithms
* 3. Generate the reference Pareto sets and Pareto fronts 4. Compute the quality indicators 5.
* Generate Latex tables reporting means and medians 6. Generate Latex tables with the result of
* applying the Wilcoxon Rank Sum Test 7. Generate Latex tables with the ranking obtained by
* applying the Friedman test 8. Generate R scripts to obtain boxplots
*
* @author Antonio J. Nebro
*/
public class ZDTScalabilityIStudy2 {
private static final int INDEPENDENT_RUNS = 25;
public static void main(String[] args) throws IOException {
if (args.length != 1) {
throw new JMetalException("Needed arguments: experimentBaseDirectory");
}
String experimentBaseDirectory = args[0];
List> problemList = new ArrayList<>();
problemList.add(new ExperimentProblem<>(new ZDT1(10), "ZDT110"));
problemList.add(new ExperimentProblem<>(new ZDT1(20), "ZDT120"));
problemList.add(new ExperimentProblem<>(new ZDT1(30), "ZDT130"));
problemList.add(new ExperimentProblem<>(new ZDT1(40), "ZDT140"));
problemList.add(new ExperimentProblem<>(new ZDT1(50), "ZDT150"));
List>> algorithmList =
configureAlgorithmList(problemList);
Experiment> experiment =
new ExperimentBuilder>("ZDTScalabilityStudy")
.setAlgorithmList(algorithmList)
.setProblemList(problemList)
.setExperimentBaseDirectory(experimentBaseDirectory)
.setOutputParetoFrontFileName("FUN")
.setOutputParetoSetFileName("VAR")
.setReferenceFrontDirectory("/pareto_fronts")
.setReferenceFrontDirectory(
experimentBaseDirectory + "/ZDTScalabilityStudy/referenceFronts")
.setIndicatorList(Arrays.asList(
new Epsilon(),
new Spread(),
new GenerationalDistance(),
new PISAHypervolume(),
new InvertedGenerationalDistance(),
new InvertedGenerationalDistancePlus()))
.setIndependentRuns(INDEPENDENT_RUNS)
.setNumberOfCores(8)
.build();
new ExecuteAlgorithms<>(experiment).run();
new GenerateReferenceParetoSetAndFrontFromDoubleSolutions(experiment).run();
new ComputeQualityIndicators<>(experiment).run();
new GenerateLatexTablesWithStatistics(experiment).run();
new GenerateWilcoxonTestTablesWithR<>(experiment).run();
new GenerateFriedmanTestTables<>(experiment).run();
new GenerateBoxplotsWithR<>(experiment).setRows(3).setColumns(3).run();
}
/**
* The algorithm list is composed of pairs {@link Algorithm} + {@link Problem} which form part of
* a {@link ExperimentAlgorithm}, which is a decorator for class {@link Algorithm}. The {@link
* ExperimentAlgorithm} has an optional tag component, that can be set as it is shown in this
* example, where four variants of a same algorithm are defined.
*/
static List>> configureAlgorithmList(
List> problemList) {
List>> algorithms = new ArrayList<>();
for (int run = 0; run < INDEPENDENT_RUNS; run++) {
for (int i = 0; i < problemList.size(); i++) {
double mutationProbability = 1.0 / problemList.get(i).getProblem().getNumberOfVariables();
double mutationDistributionIndex = 20.0;
Algorithm> algorithm = new SMPSOBuilder(
(DoubleProblem) problemList.get(i).getProblem(),
new CrowdingDistanceArchive(100))
.setMutation(new PolynomialMutation(mutationProbability, mutationDistributionIndex))
.setMaxIterations(250)
.setSwarmSize(100)
.setSolutionListEvaluator(new SequentialSolutionListEvaluator())
.build();
algorithms.add(new ExperimentAlgorithm<>(algorithm, problemList.get(i), run));
}
for (int i = 0; i < problemList.size(); i++) {
Algorithm> algorithm = new NSGAIIBuilder(
problemList.get(i).getProblem(),
new SBXCrossover(1.0, 20.0),
new PolynomialMutation(1.0 / problemList.get(i).getProblem().getNumberOfVariables(),
20.0))
.build();
algorithms.add(new ExperimentAlgorithm<>(algorithm, problemList.get(i), run));
}
for (int i = 0; i < problemList.size(); i++) {
Algorithm> algorithm = new SPEA2Builder(
problemList.get(i).getProblem(),
new SBXCrossover(1.0, 10.0),
new PolynomialMutation(1.0 / problemList.get(i).getProblem().getNumberOfVariables(),
20.0))
.build();
algorithms.add(new ExperimentAlgorithm<>(algorithm, problemList.get(i), run));
}
}
return algorithms;
}
}
