/**
* 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