/** Initialize all parameter of the algorithm */
public void initParams() {
swarmSize_ = ((Integer) getInputParameter("swarmSize")).intValue();
archiveSize_ = ((Integer) getInputParameter("archiveSize")).intValue();
maxIterations_ = ((Integer) getInputParameter("maxIterations")).intValue();
indicators_ = (QualityIndicator) getInputParameter("indicators");
polynomialMutation_ = operators_.get("mutation");
parallelEvaluator_.startEvaluator(problem_);
iteration_ = 1;
success_ = false;
particles_ = new SolutionSet(swarmSize_);
best_ = new Solution[swarmSize_];
// Create comparators for dominance and crowding distance
dominance_ = new DominanceComparator();
crowdingDistanceComparator_ = new CrowdingDistanceComparator();
distance_ = new Distance();
// Create the speed_ vector
speed_ = new double[swarmSize_][problem_.getNumberOfVariables()];
deltaMax_ = new double[problem_.getNumberOfVariables()];
deltaMin_ = new double[problem_.getNumberOfVariables()];
for (int i = 0; i < problem_.getNumberOfVariables(); i++) {
deltaMax_[i] = (problem_.getUpperLimit(i) - problem_.getLowerLimit(i)) / 2.0;
deltaMin_[i] = -deltaMax_[i];
} // for
leaders_ = new DominanceAndDecompositionArchive(archiveSize_, problem_.getNumberOfObjectives());
} // initParams
/**
* Runs of the SMPSO 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 {
initParams();
success_ = false;
// ->Step 1 (and 3) Create the initial population and evaluate
for (int i = 0; i < swarmSize_; i++) {
Solution particle = new Solution(problem_);
particles_.add(particle);
parallelEvaluator_.addSolutionForEvaluation(particle);
}
parallelEvaluator_.parallelEvaluation();
// -> Step2. Initialize the speed_ of each particle to 0
for (int i = 0; i < swarmSize_; i++) {
for (int j = 0; j < problem_.getNumberOfVariables(); j++) {
speed_[i][j] = 0.0;
}
}
// Step4 and 5
for (int i = 0; i < particles_.size(); i++) {
Solution particle = new Solution(particles_.get(i));
leaders_.add(particle);
}
// -> Step 6. Initialize the memory of each particle
for (int i = 0; i < particles_.size(); i++) {
Solution particle = new Solution(particles_.get(i));
best_[i] = particle;
}
// Crowding the leaders_
distance_.crowdingDistanceAssignment(leaders_, problem_.getNumberOfObjectives());
// -> Step 7. Iterations ..
while (iteration_ < maxIterations_) {
try {
// Compute the speed_
computeSpeed(iteration_, maxIterations_);
} catch (IOException ex) {
Logger.getLogger(SMPSODDSteadyState.class.getName()).log(Level.SEVERE, null, ex);
}
// Compute the new positions for the particles_
computeNewPositions();
// Mutate the particles_
mopsoMutation(iteration_, maxIterations_);
for (int i = 0; i < particles_.size(); i++) {
Solution particle = particles_.get(i);
parallelEvaluator_.addSolutionForEvaluation(particle);
}
parallelEvaluator_.parallelEvaluation();
// Actualize the archive
for (int i = 0; i < particles_.size(); i++) {
Solution particle = new Solution(particles_.get(i));
leaders_.add(particle);
}
// Actualize the memory of this particle
for (int i = 0; i < particles_.size(); i++) {
int flag = dominance_.compare(particles_.get(i), best_[i]);
if (flag != 1) { // the new particle is best_ than the older remeber
Solution particle = new Solution(particles_.get(i));
best_[i] = particle;
}
}
// Assign crowding distance to the leaders_
distance_.crowdingDistanceAssignment(leaders_, problem_.getNumberOfObjectives());
iteration_++;
}
parallelEvaluator_.stopEvaluator();
return this.leaders_;
} // execute