public void init() {
// System.out.println("TRIBES.init()");
// Generate a swarm
swarm =
new TribesSwarm(
this, range, initRange); // TODO initRange is hard coded equal to problem range
// swarm.generateSwarm(initExplorerNb, initType, m_problem);
// swarm.displaySwarm(swarm,out);
// print("\n Best after init: "+swarm.Best.position.fitness,out);
iter = 0;
adapt = 0;
informOption = -1;
// Hard coded option
// -1 = absolute best informant
// 1 = relative (pseudo-gradient) best informant. For "niching"
// See also moveExplorer, which can be modified in order to avoid this parameter
population.clear();
population.addAll(swarm.toPopulation());
population.init(); // necessary to allow for multi-runs
if (m_Show) show();
}
public void optimize() {
int initOption = 0;
if (iter == 0) { // first iteration!
if (initRange == null) {
rangeInitType = 0;
} else {
rangeInitType = 1; // 1 means in initRange for a start
}
// * initOption Options: 0 - random, 1 - on the bounds, 2 - sunny spell, 3 - around a center
// * rangeInitType for options 0,1: 1 means use initRange, 0 use default range
swarm.generateSwarm(initExplorerNb, initOption, rangeInitType, m_problem);
}
iter++;
m_problem.evaluatePopulationStart(population);
swarm.setSwarmSize();
// swarm.Best.positionPrev = swarm.Best.position;
swarm.moveSwarm(
range, new TribesParam(), informOption, m_problem); // *** HERE IT MOVES and EVALUATES
// public void moveSwarm(double[][] range, int fitnessSize, TribesParam pb, TribesSwarm swarm,
// int informOption, AbstractOptimizationProblem prob) {
if (Tribes.adaptOption != 0) { // perform adaption
if (Tribes.adaptOption == 1) { // Just reinitialize the swarm
adaptThreshold = iter - adapt;
// adaptMax=swarmSize;
adaptMax = swarm.linkNb(swarm);
if (adaptThreshold >= adaptMax) {
if (swarm.getBestMemory().getPrevPos().getTotalError()
<= swarm.getBestMemory().getPos().getTotalError()) {
adapt = iter; // Memorize at which iteration adaptation occurs
for (int i = 0; i < swarm.getTribeCnt(); i++) {
swarm.reinitTribe(i, rangeInitType, m_problem);
}
}
}
} else // if(swarm.Best.positionPrev.getTotalError()<=swarm.Best.position.getTotalError())
{
// Structural adaptations
adaptThreshold = iter - adapt;
// adaptMax=swarmSize;
adaptMax = swarm.linkNb(swarm);
if (adaptThreshold >= adaptMax) {
adapt = iter; // Memorize at which iteration adaptation occurs
swarm.adaptSwarm(rangeInitType, m_problem); // Re´alise l'adaptation
}
}
}
population.clear();
population.addAll(swarm.toPopulation());
if (m_Show) plotAll(population);
m_problem.evaluatePopulationEnd(population);
// this.population.incrFunctionCallsby(evals);
this.population.incrGeneration();
// this.firePropertyChangedEvent("NextGenerationPerformed"); // This is now done implicitely, as
// after every evaluation, addEvals is called
if (TRACE) {
System.out.println("loop finished after " + population.getFunctionCalls() + " evaluations");
// for (int i=0; i<population.size(); i++) System.out.println(" *
// "+((TribesExplorer)population.get(i)).getStringRepresentation());
System.out.println(
" best: "
+ population.getBestEAIndividual().getStringRepresentation()
+ " - "
+ population.getBestEAIndividual().getFitness(0));
System.out.println(
"swarm contains " + swarm.numParticles() + " particles in iteration " + iter);
}
}