/**
* Constructor. Creates a new instance of Spea2Fitness for a given <code>SolutionSet</code>.
*
* @param solutionSet The <code>SolutionSet</code>
*/
public Spea2Fitness(SolutionSet solutionSet) {
distance = distance_.distanceMatrix(solutionSet);
solutionSet_ = solutionSet;
for (int i = 0; i < solutionSet_.size(); i++) {
solutionSet_.get(i).setLocation(i);
} // for
} // Spea2Fitness
/**
* Performs the operation
*
* @param object Object representing a SolutionSet.
* @return the best solution found
*/
public Object execute(Object object, CITO_CAITO problem) {
SolutionSet solutionSet = (SolutionSet) object;
if (solutionSet.size() == 0) {
return null;
}
int bestSolution;
bestSolution = 0;
for (int i = 1; i < solutionSet.size(); i++) {
if (comparator_.compare(solutionSet.get(i), solutionSet.get(bestSolution)) < 0)
bestSolution = i;
} // for
return bestSolution;
} // Execute
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
/**
* Gets 'size' elements from a population of more than 'size' elements using for this de
* enviromentalSelection truncation
*
* @param size The number of elements to get.
*/
public SolutionSet environmentalSelection(int size) {
if (solutionSet_.size() < size) {
size = solutionSet_.size();
}
// Create a new auxiliar population for no alter the original population
SolutionSet aux = new SolutionSet(solutionSet_.size());
int i = 0;
while (i < solutionSet_.size()) {
if (solutionSet_.get(i).getFitness() < 1.0) {
aux.add(solutionSet_.get(i));
solutionSet_.remove(i);
} else {
i++;
} // if
} // while
if (aux.size() < size) {
Comparator comparator = new FitnessComparator();
solutionSet_.sort(comparator);
int remain = size - aux.size();
for (i = 0; i < remain; i++) {
aux.add(solutionSet_.get(i));
}
return aux;
} else if (aux.size() == size) {
return aux;
}
double[][] distance = distance_.distanceMatrix(aux);
List<List<DistanceNode>> distanceList = new LinkedList<List<DistanceNode>>();
for (int pos = 0; pos < aux.size(); pos++) {
aux.get(pos).setLocation(pos);
List<DistanceNode> distanceNodeList = new ArrayList<DistanceNode>();
for (int ref = 0; ref < aux.size(); ref++) {
if (pos != ref) {
distanceNodeList.add(new DistanceNode(distance[pos][ref], ref));
} // if
} // for
distanceList.add(distanceNodeList);
} // for
for (int q = 0; q < distanceList.size(); q++) {
Collections.sort(distanceList.get(q), distanceNodeComparator);
} // for
while (aux.size() > size) {
double minDistance = Double.MAX_VALUE;
int toRemove = 0;
i = 0;
Iterator<List<DistanceNode>> iterator = distanceList.iterator();
while (iterator.hasNext()) {
List<DistanceNode> dn = iterator.next();
if (dn.get(0).getDistance() < minDistance) {
toRemove = i;
minDistance = dn.get(0).getDistance();
// i y toRemove have the same distance to the first solution
} else if (dn.get(0).getDistance() == minDistance) {
int k = 0;
while ((dn.get(k).getDistance() == distanceList.get(toRemove).get(k).getDistance())
&& k < (distanceList.get(i).size() - 1)) {
k++;
}
if (dn.get(k).getDistance() < distanceList.get(toRemove).get(k).getDistance()) {
toRemove = i;
} // if
} // if
i++;
} // while
int tmp = aux.get(toRemove).getLocation();
aux.remove(toRemove);
distanceList.remove(toRemove);
Iterator<List<DistanceNode>> externIterator = distanceList.iterator();
while (externIterator.hasNext()) {
Iterator<DistanceNode> interIterator = externIterator.next().iterator();
while (interIterator.hasNext()) {
if (interIterator.next().getReference() == tmp) {
interIterator.remove();
continue;
} // if
} // while
} // while
} // while
return aux;
} // environmentalSelection
/** Assigns fitness for all the solutions. */
public void fitnessAssign() {
double[] strength = new double[solutionSet_.size()];
double[] rawFitness = new double[solutionSet_.size()];
double kDistance;
// Calculate the strength value
// strength(i) = |{j | j <- SolutionSet and i dominate j}|
for (int i = 0; i < solutionSet_.size(); i++) {
for (int j = 0; j < solutionSet_.size(); j++) {
if (dominance_.compare(solutionSet_.get(i), solutionSet_.get(j)) == -1) {
strength[i] += 1.0;
} // if
} // for
} // for
// Calculate the raw fitness
// rawFitness(i) = |{sum strenght(j) | j <- SolutionSet and j dominate i}|
for (int i = 0; i < solutionSet_.size(); i++) {
for (int j = 0; j < solutionSet_.size(); j++) {
if (dominance_.compare(solutionSet_.get(i), solutionSet_.get(j)) == 1) {
rawFitness[i] += strength[j];
} // if
} // for
} // for
// Add the distance to the k-th individual. In the reference paper of SPEA2,
// k = sqrt(population.size()), but a value of k = 1 recommended. See
// http://www.tik.ee.ethz.ch/pisa/selectors/spea2/spea2_documentation.txt
int k = 1;
for (int i = 0; i < distance.length; i++) {
Arrays.sort(distance[i]);
kDistance = 1.0 / (distance[i][k] + 2.0); // Calcule de D(i) distance
// population.get(i).setFitness(rawFitness[i]);
solutionSet_.get(i).setFitness(rawFitness[i] + kDistance);
} // for
} // fitnessAsign
/**
* @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
/**
* @author Juanjo This method selects N solutions from a set M, where N <= M using the same method
* proposed by Qingfu Zhang, W. Liu, and Hui Li in the paper describing MOEA/D-DRA (CEC 09
* COMPTETITION) An example is giving in that paper for two objectives. If N = 100, then the
* best solutions attenting to the weights (0,1), (1/99,98/99), ...,(98/99,1/99), (1,0) are
* selected.
* <p>Using this method result in 101 solutions instead of 100. We will just compute 100 even
* distributed weights and used them. The result is the same
* <p>In case of more than two objectives the procedure is: 1- Select a solution at random 2-
* Select the solution from the population which have maximum distance to it (whithout
* considering the already included)
* @param n: The number of solutions to return
* @return A solution set containing those elements
*/
SolutionSet finalSelection(int n) throws JMException {
SolutionSet res = new SolutionSet(n);
if (problem_.getNumberOfObjectives() == 2) { // subcase 1
double[][] intern_lambda = new double[n][2];
for (int i = 0; i < n; i++) {
double a = 1.0 * i / (n - 1);
intern_lambda[i][0] = a;
intern_lambda[i][1] = 1 - a;
} // for
// we have now the weights, now select the best solution for each of them
for (int i = 0; i < n; i++) {
Solution current_best = population.get(0);
int index = 0;
double value = fitnessFunction(current_best, intern_lambda[i]);
for (int j = 1; j < n; j++) {
double aux =
fitnessFunction(
population.get(j), intern_lambda[i]); // we are looking the best for the weight i
if (aux < value) { // solution in position j is better!
value = aux;
current_best = population.get(j);
}
}
res.add(new Solution(current_best));
}
} else { // general case (more than two objectives)
Distance distance_utility = new Distance();
int random_index = PseudoRandom.randInt(0, population.size() - 1);
// create a list containing all the solutions but the selected one (only references to them)
List<Solution> candidate = new LinkedList<Solution>();
candidate.add(population.get(random_index));
for (int i = 0; i < population.size(); i++) {
if (i != random_index) {
candidate.add(population.get(i));
}
} // for
while (res.size() < n) {
int index = 0;
Solution selected = candidate.get(0); // it should be a next! (n <= population size!)
double distance_value =
distance_utility.distanceToSolutionSetInObjectiveSpace(selected, res);
int i = 1;
while (i < candidate.size()) {
Solution next_candidate = candidate.get(i);
double aux =
distance_value =
distance_utility.distanceToSolutionSetInObjectiveSpace(next_candidate, res);
if (aux > distance_value) {
distance_value = aux;
index = i;
}
i++;
}
// add the selected to res and remove from candidate list
res.add(new Solution(candidate.remove(index)));
} //
}
return res;
}
/**
* @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