/**
* 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
