/** * Configure the MOCell algorithm with default parameter settings * * @return an algorithm object * @throws jmetal.util.JMException */ public Algorithm configure() throws JMException { Algorithm algorithm; Operator selection = null; // Selection operator Operator crossover = null; // Crossover operator Operator mutation = null; // Mutation operator Operator localsearch = null; // LocalSearch operator QualityIndicator indicators; // Creating the problem: there are six MOCell variants // algorithm = new sMOCell1(problem_) ; // algorithm = new sMOCell2(problem_) ; // algorithm = new aMOCell1(problem_) ; // algorithm = new aMOCell2(problem_) ; // algorithm = new aMOCell3(problem_) ; // algorithm = new aMOCell4CTBT(problem_) ; algorithm = new aMOCell4(problem_); // algorithm = new aMOCell2CTBT(problem_) ; // Algorithm parameters algorithm.setInputParameter("populationSize", populationSize_); algorithm.setInputParameter("maxEvaluations", maxEvaluations_); algorithm.setInputParameter("archiveSize", archiveSize_); algorithm.setInputParameter("feedBack", feedback_); // algorithm.setInputParameter( "specialSolution" , P_SPECIAL_SOLUTION ); // Mutation and Crossover for Real codification crossover = CrossoverFactory.getCrossoverOperator(CROSSOVER); // crossover = CrossoverFactory.getCrossoverOperator("SBXCrossover"); crossover.setParameter("probability", crossoverProbability_); crossover.setParameter("distributionIndex", distributionIndexForCrossover_); mutation = MutationFactory.getMutationOperator(MUTATION); // mutation = MutationFactory.getMutationOperator("PolynomialMutation"); mutation.setParameter("probability", mutationProbability_); // mutation.setParameter( "rounds" , (Integer) M_ROUNDS ); mutation.setParameter("Problem", problem_); // mutation.setParameter( "overloadPercentage" , M_OVERLOAD_PER ); // mutation.setParameter( "Policy" , M_POLICY ); // mutation.setParameter( "Mode" , M_MODE ); selection = SelectionFactory.getSelectionOperator(SELECTION); localsearch = LocalSearchFactory.getLocalSearchOperator(LOCAL_SEARCH); localsearch.setParameter("Problem", problem_); // Add the operators to the algorithm algorithm.addOperator("localsearch", localsearch); algorithm.addOperator("crossover", crossover); algorithm.addOperator("mutation", mutation); algorithm.addOperator("selection", selection); // // Creating the indicator object // if (! paretoFrontFile_.equals("")) { // indicators = new QualityIndicator(problem_, paretoFrontFile_); // algorithm.setInputParameter("indicators", indicators) ; // } // if return algorithm; }
/** * Configure the MOCell algorithm with default parameter settings * * @return an algorithm object * @throws jmetal.util.JMException */ public Algorithm configure() throws JMException { Algorithm algorithm; Operator selection; Operator crossover; Operator mutation; QualityIndicator indicators; // Creating the problem: there are six MOCell variants // algorithm = new sMOCell1(problem_) ; // algorithm = new sMOCell2(problem_) ; // algorithm = new aMOCell1(problem_) ; // algorithm = new aMOCell2(problem_) ; // algorithm = new aMOCell3(problem_) ; algorithm = new sMOCell4CTBT(problem_); // Algorithm parameters algorithm.setInputParameter("populationSize", populationSize_); algorithm.setInputParameter("maxEvaluations", maxEvaluations_); algorithm.setInputParameter("archiveSize", archiveSize_); algorithm.setInputParameter("feedBack", feedback_); // Mutation and Crossover for Real codification crossover = CrossoverFactory.getCrossoverOperator("DPX"); // crossover = CrossoverFactory.getCrossoverOperator("SBXCrossover"); crossover.setParameter("probability", crossoverProbability_); crossover.setParameter("distributionIndex", distributionIndexForCrossover_); mutation = MutationFactory.getMutationOperator("BinaryMutation"); // mutation = MutationFactory.getMutationOperator("PolynomialMutation"); mutation.setParameter("probability", mutationProbability_); mutation.setParameter("distributionIndex", distributionIndexForMutation_); // Selection Operator selection = SelectionFactory.getSelectionOperator("BinaryTournament"); // Add the operators to the algorithm algorithm.addOperator("crossover", crossover); algorithm.addOperator("mutation", mutation); algorithm.addOperator("selection", selection); // // Creating the indicator object // if (! paretoFrontFile_.equals("")) { // indicators = new QualityIndicator(problem_, paretoFrontFile_); // algorithm.setInputParameter("indicators", indicators) ; // } // if return algorithm; }
public static void main(String[] args) throws JMException, 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 // int bits ; // Length of bit string in the OneMax problem // bits = 512 ; // problem = new OneMax(bits); problem = new Sphere("Real", 20); // problem = new Easom("Real") ; // problem = new Griewank("Real", 10) ; algorithm = new DE(problem); // Asynchronous cGA /* Algorithm parameters*/ algorithm.setInputParameter("populationSize", 100); algorithm.setInputParameter("maxEvaluations", 1000000); // Crossover operator crossover = CrossoverFactory.getCrossoverOperator("DifferentialEvolutionCrossover"); crossover.setParameter("CR", 0.1); crossover.setParameter("F", 0.5); crossover.setParameter("DE_VARIANT", "rand/1/bin"); // Add the operators to the algorithm selection = SelectionFactory.getSelectionOperator("DifferentialEvolutionSelection"); algorithm.addOperator("crossover", crossover); algorithm.addOperator("selection", selection); /* Execute the Algorithm */ long initTime = System.currentTimeMillis(); SolutionSet population = algorithm.execute(); long estimatedTime = System.currentTimeMillis() - initTime; System.out.println("Total execution time: " + estimatedTime); /* Log messages */ 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
/** * Configure method. Change the default configuration * * @param settings * @return A problem with the settings indicated as argument * @throws jmetal.util.JMException * @throws ClassNotFoundException */ public final Algorithm configure(Properties settings) throws JMException, IllegalArgumentException, IllegalAccessException, ClassNotFoundException { if (settings != null) { Field[] fields = this.getClass().getFields(); for (int i = 0; i < fields.length; i++) { if (fields[i].getName().endsWith("_")) { // it is a configuration field // The configuration field is an integer if (fields[i].getType().equals(int.class) || fields[i].getType().equals(Integer.class)) { int value = Integer.parseInt( settings.getProperty(fields[i].getName(), "" + fields[i].getInt(this))); fields[i].setInt(this, value); } else if (fields[i].getType().equals(double.class) || fields[i].getType().equals(Double.class)) { // The configuration field is a double double value = Double.parseDouble( settings.getProperty(fields[i].getName(), "" + fields[i].getDouble(this))); if (fields[i].getName().equals("mutationProbability_") && value == 0) { if ((problem_.getSolutionType().getClass() == Class.forName("jmetal.base.solutionType.RealSolutionType")) || (problem_.getSolutionType().getClass() == Class.forName("jmetal.base.solutionType.ArrayRealSolutionType"))) { value = 1.0 / problem_.getNumberOfVariables(); } else if (problem_.getSolutionType().getClass() == Class.forName("jmetal.base.solutionType.BinarySolutionType") || problem_.getSolutionType().getClass() == Class.forName("jmetal.base.solutionType.BinaryRealSolutionType")) { int length = problem_.getNumberOfBits(); value = 1.0 / length; System.out.println("La probabilidad es : " + value); } else { int length = 0; for (int j = 0; j < problem_.getNumberOfVariables(); j++) { length += problem_.getLength(j); } value = 1.0 / length; } fields[i].setDouble(this, value); } // if else { fields[i].setDouble(this, value); } } else { Object value = settings.getProperty(fields[i].getName(), null); if (value != null) { if (fields[i].getType().equals(jmetal.base.operator.crossover.Crossover.class)) { Object value2 = CrossoverFactory.getCrossoverOperator((String) value, settings); value = value2; } if (fields[i].getType().equals(jmetal.base.operator.mutation.Mutation.class)) { Object value2 = MutationFactory.getMutationOperator((String) value, settings); value = value2; } fields[i].set(this, value); } } } } // for // At this point all the fields have been read from the properties // parameter. Those fields representing crossover and mutations should also // be initialized. However, there is still mandatory to configure them for (int i = 0; i < fields.length; i++) { if (fields[i].getType().equals(jmetal.base.operator.crossover.Crossover.class) || fields[i].getType().equals(jmetal.base.operator.mutation.Mutation.class)) { Operator operator = (Operator) fields[i].get(this); // This field stores a crossover operator String tmp = fields[i].getName(); String aux = fields[i].getName().substring(0, tmp.length() - 1); for (int j = 0; j < fields.length; j++) { if (i != j) { if (fields[j].getName().startsWith(aux)) { // The field is a configuration parameter of the crossover tmp = fields[j].getName().substring(aux.length(), fields[j].getName().length() - 1); if ((fields[j].get(this) != null)) { System.out.println(fields[j].getName()); if (fields[j].getType().equals(int.class) || fields[j].getType().equals(Integer.class)) { operator.setParameter(tmp, fields[j].getInt(this)); } else if (fields[j].getType().equals(double.class) || fields[j].getType().equals(Double.class)) { operator.setParameter(tmp, fields[j].getDouble(this)); } } } } } } } // At this point, we should compare if the pareto front have been added paretoFrontFile_ = settings.getProperty("paretoFrontFile_", ""); } return configure(); } // configure
/** * @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, 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 QualityIndicator indicators; // Object to get quality indicators // Logger object and file to store log messages logger_ = Configuration.logger_; fileHandler_ = new FileHandler("IBEA.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 algorithm = new IBEA(problem); // Algorithm parameters algorithm.setInputParameter("populationSize", 100); algorithm.setInputParameter("archiveSize", 100); algorithm.setInputParameter("maxEvaluations", 25000); // Mutation and Crossover for Real codification crossover = CrossoverFactory.getCrossoverOperator("SBXCrossover"); crossover.setParameter("probability", 1.0); crossover.setParameter("distribuitionIndex", 20.0); mutation = MutationFactory.getMutationOperator("PolynomialMutation"); mutation.setParameter("probability", 1.0 / problem.getNumberOfVariables()); mutation.setParameter("distributionIndex", 20.0); /* Mutation and Crossover Binary codification */ /* crossover = CrossoverFactory.getCrossoverOperator("SinglePointCrossover"); crossover.setParameter("probability",0.9); mutation = MutationFactory.getMutationOperator("BitFlipMutation"); mutation.setParameter("probability",1.0/80); */ /* Selection Operator */ selection = new BinaryTournament(new FitnessComparator()); // Add the operators to the algorithm algorithm.addOperator("crossover", crossover); algorithm.addOperator("mutation", mutation); algorithm.addOperator("selection", selection); // Execute the Algorithm long initTime = System.currentTimeMillis(); SolutionSet population = algorithm.execute(); long estimatedTime = System.currentTimeMillis() - initTime; // 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