/**
* Creates a LZ09_F2 problem instance
*
* @param solutionType The solution type must "Real" or "BinaryReal".
*/
public LZ09_F2(String solutionType, Integer ptype, Integer dtype, Integer ltype)
throws ClassNotFoundException {
numberOfVariables_ = 30;
numberOfObjectives_ = 2;
numberOfConstraints_ = 0;
problemName_ = "LZ09_F2";
LZ09_ = new LZ09(numberOfVariables_, numberOfObjectives_, ptype, dtype, ltype);
lowerLimit_ = new double[numberOfVariables_];
upperLimit_ = new double[numberOfVariables_];
lowerLimit_[0] = 0.0;
upperLimit_[0] = 1.0;
for (int var = 1; var < numberOfVariables_; var++) {
lowerLimit_[var] = -1.0;
upperLimit_[var] = 1.0;
} // for
if (solutionType.compareTo("BinaryReal") == 0) solutionType_ = new BinaryRealSolutionType(this);
else if (solutionType.compareTo("Real") == 0) solutionType_ = new RealSolutionType(this);
else {
System.out.println("Error: solution type " + solutionType + " invalid");
System.exit(-1);
}
} // LZ09_F2
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
double fitnessFunction(Solution individual, double[] lambda) {
double fitness;
fitness = 0.0;
if (functionType_.equals("_TCHE1")) {
double maxFun = -1.0e+30;
for (int n = 0; n < problem_.getNumberOfObjectives(); n++) {
double diff = Math.abs(individual.getObjective(n) - z_[n]);
double feval;
if (lambda[n] == 0) {
feval = 0.0001 * diff;
} else {
feval = diff * lambda[n];
}
if (feval > maxFun) {
maxFun = feval;
}
} // for
fitness = maxFun;
} // if
else if (functionType_.equals("_AGG")) {
double sum = 0.0;
for (int n = 0; n < problem_.getNumberOfObjectives(); n++) {
sum += (lambda[n]) * individual.getObjective(n);
}
fitness = sum;
} else if (functionType_.equals("_PBI")) {
double d1, d2, nl;
double theta = 5.0;
d1 = d2 = nl = 0.0;
for (int i = 0; i < problem_.getNumberOfObjectives(); i++) {
d1 += (individual.getObjective(i) - z_[i]) * lambda[i];
nl += Math.pow(lambda[i], 2.0);
}
nl = Math.sqrt(nl);
d1 = Math.abs(d1) / nl;
for (int i = 0; i < problem_.getNumberOfObjectives(); i++) {
d2 += Math.pow((individual.getObjective(i) - z_[i]) - d1 * (lambda[i] / nl), 2.0);
}
d2 = Math.sqrt(d2);
fitness = (d1 + theta * d2);
} else {
System.out.println("MOEAD.fitnessFunction: unknown type " + functionType_);
System.exit(-1);
}
return fitness;
} // fitnessEvaluation
public void readProblem(String fileName) throws FileNotFoundException, IOException {
Reader inputFile = new BufferedReader(new InputStreamReader(new FileInputStream(fileName)));
StreamTokenizer token = new StreamTokenizer(inputFile);
try {
token.nextToken();
numberOfCities_ = (int) token.nval;
distanceMatrix_ = new double[numberOfCities_][numberOfCities_];
flujo1 = new double[numberOfCities_][numberOfCities_];
flujo2 = new double[numberOfCities_][numberOfCities_];
// Cargar objetivo 1
for (int k = 0; k < numberOfCities_; k++) {
for (int j = 0; j < numberOfCities_; j++) {
token.nextToken();
flujo1[k][j] = token.nval;
}
}
// Cargar objetivo 2
for (int k = 0; k < numberOfCities_; k++) {
for (int j = 0; j < numberOfCities_; j++) {
token.nextToken();
flujo2[k][j] = token.nval;
}
}
// Carga de distancias
for (int k = 0; k < numberOfCities_; k++) {
for (int j = 0; j < numberOfCities_; j++) {
token.nextToken();
distanceMatrix_[k][j] = token.nval;
}
}
} // try
catch (Exception e) {
System.err.println("QAP.readProblem(): error when reading data file " + e);
System.exit(1);
} // catch
} // readProblem
/**
* Constructor. Creates a default instance of the Srinivas problem
*
* @param solutionType The solution type must "Real" or "BinaryReal".
*/
public Srinivas(String solutionType) throws ClassNotFoundException {
numberOfVariables_ = 2;
numberOfObjectives_ = 2;
numberOfConstraints_ = 2;
problemName_ = "Srinivas";
lowerLimit_ = new double[numberOfVariables_];
upperLimit_ = new double[numberOfVariables_];
for (int var = 0; var < numberOfVariables_; var++) {
lowerLimit_[var] = -20.0;
upperLimit_[var] = 20.0;
} // for
if (solutionType.compareTo("BinaryReal") == 0) solutionType_ = new BinaryRealSolutionType(this);
else if (solutionType.compareTo("Real") == 0) solutionType_ = new RealSolutionType(this);
else {
System.out.println("Error: solution type " + solutionType + " invalid");
System.exit(-1);
}
} // Srinivas
public void initNeighborhood() {
double[] x = new double[populationSize];
int[] idx = new int[populationSize];
for (int i = 0; i < populationSize; i++) {
// calculate the distances based on weight vectors
for (int j = 0; j < populationSize; j++) {
x[j] = Utils.distVector(lambda_[i], lambda_[j]);
// x[j] = dist_vector(population[i].namda,population[j].namda);
idx[j] = j;
// System.out.println("x["+j+"]: "+x[j]+ ". idx["+j+"]: "+idx[j]) ;
} // for
// find 'niche' nearest neighboring subproblems
Utils.minFastSort(x, idx, populationSize, T_);
// minfastsort(x,idx,population.size(),niche);
System.arraycopy(idx, 0, neighborhood_[i], 0, T_);
} // for
} // initNeighborhood
/**
* Creates a new DTLZ5 problem instance
*
* @param numberOfVariables Number of variables
* @param numberOfObjectives Number of objective functions
* @param solutionType The solution type must "Real" or "BinaryReal".
*/
public DTLZ5(String solutionType, Integer numberOfVariables, Integer numberOfObjectives)
throws ClassNotFoundException {
numberOfVariables_ = numberOfVariables.intValue();
numberOfObjectives_ = numberOfObjectives.intValue();
numberOfConstraints_ = 0;
problemName_ = "DTLZ5";
lowerLimit_ = new double[numberOfVariables_];
upperLimit_ = new double[numberOfVariables_];
for (int var = 0; var < numberOfVariables_; var++) {
lowerLimit_[var] = 0.0;
upperLimit_[var] = 1.0;
}
if (solutionType.compareTo("BinaryReal") == 0) solutionType_ = new BinaryRealSolutionType(this);
else if (solutionType.compareTo("Real") == 0) solutionType_ = new RealSolutionType(this);
else {
System.out.println("Error: solution type " + solutionType + " invalid");
System.exit(-1);
}
} // DTLZ5
/**
* Creates a new instance of problem CEC2009_UF10.
*
* @param numberOfVariables Number of variables.
* @param solutionType The solution type must "Real" or "BinaryReal".
*/
public CEC2009_UF10(String solutionType, Integer numberOfVariables)
throws ClassNotFoundException {
numberOfVariables_ = numberOfVariables.intValue();
numberOfObjectives_ = 3;
numberOfConstraints_ = 0;
problemName_ = "CEC2009_UF10";
upperLimit_ = new double[numberOfVariables_];
lowerLimit_ = new double[numberOfVariables_];
// Establishes upper and lower limits for the variables
for (int var = 0; var < numberOfVariables_; var++) {
lowerLimit_[var] = 0.0;
upperLimit_[var] = 1.0;
} // for
if (solutionType.compareTo("BinaryReal") == 0) solutionType_ = new BinaryRealSolutionType(this);
else if (solutionType.compareTo("Real") == 0) solutionType_ = new RealSolutionType(this);
else {
System.out.println("Error: solution type " + solutionType + " invalid");
System.exit(-1);
}
} // CEC2009_UF10
/**
* @param args Command line arguments. The first (optional) argument specifies the problem to
* solve.
* @throws JMException
* @throws IOException
* @throws SecurityException Usage: three options - jmetal.metaheuristics.mocell.MOCell_main -
* jmetal.metaheuristics.mocell.MOCell_main problemName -
* jmetal.metaheuristics.mocell.MOCell_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 mutation; // Mutation operator
QualityIndicator indicators; // Object to get quality indicators
// Logger object and file to store log messages
logger_ = Configuration.logger_;
fileHandler_ = new FileHandler("PAES_main.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("ArrayReal", 3);
// problem = new Fonseca("Real");
// problem = new Kursawe("BinaryReal",3);
// problem = new Water("Real");
// problem = new ZDT4("Real", 1000);
// problem = new WFG1("Real");
// problem = new DTLZ1("Real");
// problem = new OKA2("Real") ;
} // else
algorithm = new PAES(problem);
// Algorithm parameters
algorithm.setInputParameter("archiveSize", 100);
algorithm.setInputParameter("biSections", 5);
algorithm.setInputParameter("maxEvaluations", 25000);
// Mutation (Real variables)
mutation = MutationFactory.getMutationOperator("PolynomialMutation");
mutation.setParameter("probability", 1.0 / problem.getNumberOfVariables());
mutation.setParameter("distributionIndex", 20.0);
// Mutation (BinaryReal variables)
// mutation = MutationFactory.getMutationOperator("BitFlipMutation");
// mutation.setParameter("probability",0.1);
// Add the operators to the algorithm
algorithm.addOperator("mutation", mutation);
// Execute the Algorithm
long initTime = System.currentTimeMillis();
SolutionSet population = algorithm.execute();
long estimatedTime = System.currentTimeMillis() - initTime;
// Result messages
// STEP 8. 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
/**
* @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
