/**
* This method is used to evaluate the first population,that it is called after the calling to
* <code>setup</code> method
*
* @throws JMException
*/
public void evaluatePopulation() throws JMException {
// NOTEIT First evaluation of the SolutionSet
int loadingPosition = population_.getLoadingPosition();
// Commented by Bernabe Dorronsoro
// if ( ( specialSolution_ != null ) ){
//
// DecisionVariables[] best = new DecisionVariables[problem_.getNumberOfIslands()-1];
//
// population_.clear();
// for( int f=0 ; f<populationSize_ ; ++f) {
// DecisionVariables specialDV = problem_.generateSpecial( specialSolution_ );
// if ( f < numberOfSolutions_ ){
// for( int i=0 ; i<problem_.getNumberOfIslands() ; ++i ){
// if (i<loadingPosition)
// best[i] = new DecisionVariables( specialDV.extractSlice(i) );
// if (i>loadingPosition)
// best[i-1] = new DecisionVariables( specialDV.extractSlice(i) );
// } // for
//
// population_.setBestRow( best , f );
// } // if
//
// DecisionVariables decisionVar = new DecisionVariables( specialDV.extractSlice(
// loadingPosition ) );
// Solution S = new Solution( problem_ , decisionVar );
// population_.add( S );
// S.unLink();
// } // for
//
// } // if
Iterator<Solution> it = population_.iterator();
while (it.hasNext()) {
Solution tmp = it.next();
tmp.unLink();
population_.linkExternalDecisionVariables(tmp);
problem_.evaluate(tmp, loadingPosition);
problem_.evaluateConstraints(tmp);
++evaluations_;
} // while
// population_.printObjectivesToFile( "FUN.S."+loadingPosition );
} // evaluatePopulation
public void setup(int islandId) throws JMException {
islandID_ = islandId;
distance_ = new Distance();
// Read the parameters
indicators_ = (QualityIndicator) this.getInputParameter("indicators");
islands_ = ((Integer) getInputParameter("numberOfIslands")).intValue();
populationSize_ = ((Integer) getInputParameter("populationSize")).intValue();
maxEvaluations_ = ((Integer) getInputParameter("maxEvaluations")).intValue();
numberOfSolutions_ = ((Integer) getInputParameter("numberOfSolutions")).intValue();
bestSolutionsFirstLevel_ = ((Integer) getInputParameter("bestSolutionsFirstLevel")).intValue();
specialSolution_ = ((String) getInputParameter("specialSolution"));
mergeSolution_ = ((Integer) this.getInputParameter("mergeSolution")).intValue();
// Initialize the variables
population_ =
new SolutionSet(islandId, islands_, numberOfSolutions_, populationSize_, mergeSolution_);
evaluations_ = 0;
// bestSolutions = new Solution[islands-1];
requiredEvaluations_ = 0;
// Read the operators
mutationOperator_ = operators_.get("mutation");
crossoverOperator_ = operators_.get("crossover");
selectionOperator_ = operators_.get("selection");
localSearchOperator_ = operators_.get("localsearch");
// NOTEIT Creation of the SolutionSet of the Islands
// Create the initial solutionSet
Solution newSolution = null;
// for( int i=0 ; i<populationSize_ ; ++i) {
// newSolution = new Solution( problem_ );
// population_.add( newSolution );
// } // for
int minminInit =
PseudoRandom.randInt(0, populationSize_ - 1); // To initialize one individual with min-min
for (int i = 0; i < populationSize_; i++) {
if (specialSolution_ == null) {
newSolution = new Solution(problem_);
} // if
else if (specialSolution_.contains("OneMinmin")) {
if (minminInit == i) {
// int [] vars = ScheduleStrategy.minMin(ETC_, numberOfTasks, numberOfMachines)
DecisionVariables specialDV = problem_.generateSpecial(specialSolution_, islandId);
newSolution = new Solution(problem_, specialDV);
} else newSolution = new Solution(problem_);
} else if (specialSolution_.equalsIgnoreCase("Min-Min")) {
DecisionVariables specialDV = problem_.generateSpecial(specialSolution_, islandId);
newSolution = new Solution(problem_, specialDV);
} // else
// problem_.evaluate( newSolution );
// problem_.evaluateConstraints( newSolution );
population_.add(newSolution);
newSolution.setLocation(i);
// evaluations++;
} // for
prepareSetupBestSolutions();
} // setup
