protected void crowdingDistanceSelection(Ranking ranking) {
population.clear();
int rankingIndex = 0;
while (populationIsNotFull()) {
if (subfrontFillsIntoThePopulation(ranking, rankingIndex)) {
addRankedSolutionsToPopulation(ranking, rankingIndex);
rankingIndex++;
} else {
computeCrowdingDistance(ranking, rankingIndex);
addLastRankedSolutions(ranking, rankingIndex);
}
}
}
/**
* Execute the ElitistES algorithm
* @throws JMException
*/
public SolutionSet execute() throws JMException, ClassNotFoundException {
int maxEvaluations ;
int evaluations ;
SolutionSet population ;
SolutionSet offspringPopulation ;
Operator mutationOperator ;
Comparator comparator ;
comparator = new ObjectiveComparator(0) ; // Single objective comparator
// Read the params
maxEvaluations = ((Integer)this.getInputParameter("maxEvaluations")).intValue();
// Initialize the variables
population = new SolutionSet(mu_) ;
offspringPopulation = new SolutionSet(mu_ + lambda_) ;
evaluations = 0;
// Read the operators
mutationOperator = this.operators_.get("mutation");
System.out.println("(" + mu_ + " + " + lambda_+")ES") ;
// Create the parent population of mu solutions
Solution newIndividual;
for (int i = 0; i < mu_; i++) {
newIndividual = new Solution(problem_);
evaluations++;
population.add(newIndividual);
} //for
finish {
//
async {
for (int i = 0; i < mu_; i++) {
evaluate_internal(population, i, null);
}
}
}
// Main loop
int offsprings ;
offsprings = lambda_ / mu_ ;
while (evaluations < maxEvaluations) {
// STEP 1. Generate the mu+lambda population
for (int i = 0; i < mu_; i++) {
for (int j = 0; j < offsprings; j++) {
Solution offspring = new Solution(population.get(i)) ;
offspringPopulation.add(offspring) ;
evaluations ++ ;
} // for
} // for
finish {
//
async {
for (int i = 0; i < mu_*offsprings; i++) {
evaluate_internal(offspringPopulation, i, mutationOperator);
}
}
}
// STEP 2. Add the mu individuals to the offspring population
for (int i = 0 ; i < mu_; i++) {
offspringPopulation.add(population.get(i)) ;
} // for
population.clear() ;
// STEP 3. Sort the mu+lambda population
offspringPopulation.sort(comparator) ;
// STEP 4. Create the new mu population
for (int i = 0; i < mu_; i++)
population.add(offspringPopulation.get(i)) ;
System.out.println("Evaluation: " + evaluations + " Fitness: " +
population.get(0).getObjective(0)) ;
// STEP 6. Delete the mu+lambda population
offspringPopulation.clear() ;
} // while
// Return a population with the best individual
SolutionSet resultPopulation = new SolutionSet(1) ;
resultPopulation.add(population.get(0)) ;
/**
* Runs of the PESA2 algorithm.
*
* @return a <code>SolutionSet</code> that is a set of non dominated solutions as a result of the
* algorithm execution
* @throws JMException
*/
public SolutionSet execute() throws JMException, ClassNotFoundException {
int archiveSize, bisections, maxEvaluations, evaluations, populationSize;
AdaptiveGridArchive archive;
SolutionSet solutionSet;
Operator crossover, mutation, selection;
// Read parameters
populationSize = ((Integer) (inputParameters_.get("populationSize"))).intValue();
archiveSize = ((Integer) (inputParameters_.get("archiveSize"))).intValue();
bisections = ((Integer) (inputParameters_.get("bisections"))).intValue();
maxEvaluations = ((Integer) (inputParameters_.get("maxEvaluations"))).intValue();
// Get the operators
crossover = operators_.get("crossover");
mutation = operators_.get("mutation");
// Initialize the variables
evaluations = 0;
archive = new AdaptiveGridArchive(archiveSize, bisections, problem_.getNumberOfObjectives());
solutionSet = new SolutionSet(populationSize);
HashMap parameters = null;
selection = new PESA2Selection(parameters);
// -> Create the initial individual and evaluate it and his constraints
for (int i = 0; i < populationSize; i++) {
Solution solution = new Solution(problem_);
problem_.evaluate(solution);
problem_.evaluateConstraints(solution);
solutionSet.add(solution);
}
// Incorporate non-dominated solution to the archive
for (int i = 0; i < solutionSet.size(); i++) {
archive.add(solutionSet.get(i)); // Only non dominated are accepted by
// the archive
}
// Clear the init solutionSet
solutionSet.clear();
// Iterations....
Solution[] parents = new Solution[2];
do {
// -> Create the offSpring solutionSet
while (solutionSet.size() < populationSize) {
parents[0] = (Solution) selection.execute(archive);
parents[1] = (Solution) selection.execute(archive);
Solution[] offSpring = (Solution[]) crossover.execute(parents);
mutation.execute(offSpring[0]);
problem_.evaluate(offSpring[0]);
problem_.evaluateConstraints(offSpring[0]);
evaluations++;
solutionSet.add(offSpring[0]);
}
for (int i = 0; i < solutionSet.size(); i++) archive.add(solutionSet.get(i));
// Clear the solutionSet
solutionSet.clear();
} while (evaluations < maxEvaluations);
// Return the solutionSet of non-dominated individual
return archive;
} // execute
