/**
* @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
// Logger object and file to store log messages
// logger_ = Configuration.logger_ ;
fileHandler_ = new FileHandler("NSGAII_main.log");
// logger_.addHandler(fileHandler_) ;
problem = new QOP();
int corridas;
String caso;
nrocaso = 0;
Poblacion population = new Poblacion(50);
while (nrocaso < casosDePrueba.length) {
corridas = 1;
long initTime2 = System.currentTimeMillis();
while (corridas < 11) {
algorithm = new NSGAII_G10(problem, nrocaso);
caso = casosDePrueba[nrocaso];
// algorithm = new ssNSGAII(problem);
// Algorithm parameters
algorithm.setInputParameter("populationSize", 5);
algorithm.setInputParameter("maxEvaluations", 2500);
algorithm.setInputParameter("probMutacion", 1); // 10%
algorithm.setInputParameter("nrocaso", nrocaso);
algorithm.setInputParameter("corridas", corridas);
// Mutation and Crossover for Real codification
/*parameters = new HashMap() ;
parameters.put("probability", 0.9) ;
parameters.put("distributionIndex", 20.0) ;
crossover = CrossoverFactory.getCrossoverOperator("SBXCrossover", parameters);
parameters = new HashMap() ;
parameters.put("probability", 1.0/problem.getNumberOfVariables()) ;
parameters.put("distributionIndex", 20.0) ;
mutation = MutationFactory.getMutationOperator("PolynomialMutation", parameters);
// Selection Operator
parameters = null ;
selection = SelectionFactory.getSelectionOperator("BinaryTournament2", parameters) ;
*/
// Add the operators to the algorithm
/*algorithm.addOperator("crossover",crossover);
algorithm.addOperator("mutation",mutation);
algorithm.addOperator("selection",selection);*/
// algorithm.addOperator("torneobinario", torneobinario);
// Add the indicator object to the algorithm
// System.out.println(" "+corridas);
// Execute the Algorithm
long initTime = System.currentTimeMillis();
// System.out.println(caso + "-" + corridas + " Test Genetico.");
population = algorithm.execute();
long estimatedTime = System.currentTimeMillis() - initTime;
// Result messages
if (population != null) {
// logger_.info("Total execution time: "+estimatedTime + "ms");
// logger_.info("Variables values have been writen to file VAR");
population.printVariablesToFile("VAR_p3" + caso);
// logger_.info("Objectives values have been writen to file FUN");
population.printObjectivesToFile("FUN_p3" + caso);
} else {
System.out.println("No arrojo resultados");
}
/*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*/
corridas++;
}
long estimatedTime2 = System.currentTimeMillis() - initTime2;
long tiempo = estimatedTime2;
long hora = tiempo / 3600000;
long restohora = tiempo % 3600000;
long minuto = restohora / 60000;
long restominuto = restohora % 60000;
long segundo = restominuto / 1000;
long restosegundo = restominuto % 1000;
String time = hora + ":" + minuto + ":" + segundo + "." + restosegundo;
time = " Tiempo: " + time;
String fin = casosDePrueba[nrocaso] + " FIN - Test Genetico. Tiempo:" + time;
// fin += " - Nº Generaciones: " + evaluations;
System.out.println(fin);
if (population != null) population.printFinalResults();
nrocaso++;
}
System.out.println("FIN Prueba Algoritmo Genetico. (Segment-Oriented).");
} // main
