/**
* this implements the core of the algorithm use a starter method to define the abort criteria, eg
* # iterations or duration
*
* @return best Individual that was found (valid and invalid)
* @author jochen
*/
private Individual startAlgorithm() {
startTime = System.currentTimeMillis();
// System.out.println("Start Algorithm for Problem " + problem.getInstanceName() + " (Fees: " +
// Config.getFee() +")");
/* create start population **************************************************/
if (currentPopulation == null) {
currentPopulation = new PopulationImpl(problem, populationSize);
}
// System.out.println(currentPopulation.getBestIndividual());
/* evolve ********************************************************************/
while (!stop()) {
/* create a new empty child-population ***********************************/
Population newGeneration = new PopulationImpl(this.problem);
/* adds the best individual to new generation ****************************/
newGeneration.add(currentPopulation.getBestIndividual());
/* add some random individuals from foreign countries to new population *****/
for (int j = 0; j < populationSize * Config.getPercentageForeignIndividuals(); j++) {
currentPopulation.remove(currentPopulation.getWorstIndividual());
}
for (int j = 0; j < populationSize * Config.getPercentageForeignIndividuals(); j++) {
currentPopulation.add(IndividualImpl.generateRandomIndividual(problem));
}
/* create a new generation with doubled size as current ******************/
while (newGeneration.getSize()
< populationSize * Config.getNewGenerationSize()) { // size of new population
// higher value results in higher selection pressure
/* selection *******************************************************/
Individual mum = null, dad = null;
if (Config.getSelectionMethod() == 0) { // random
mum = currentPopulation.getRandomIndividual();
dad = currentPopulation.getRandomIndividual();
} else if (Config.getSelectionMethod() == 1) { // roulette wheel
mum = currentPopulation.getIndividualByRouletteWheel();
dad = currentPopulation.getIndividualByRouletteWheel();
}
/* recombine *******************************************************/
Individual baby = mum.haveSex(dad);
/* mutate **********************************************************/
if (Math.random() < Config.getOddsMutation()) {
double random = Math.random();
if (random < 0.3) {
baby.mutateNearNeighbor();
} else if (random < 0.5) {
baby.mutateOnlyCurrentFacilities();
} else if (random < 0.7) {
for (int i = 0; i < (int) (Math.random() * 5); i++) {
baby.mutateNearNeighbor();
}
} else if (random < 0.8) {
baby.mutateSwitchCustomers();
} else if (random < 0.85) {
baby.mutateBanFacilityAndFindNewFromCurretUsed();
} else if (random < 0.9) {
baby.mutateCloseAndOpenAFacility();
} else if (random < 0.95) {
baby.mutateBanFacilityAndFindNewFacilityByRouletteWheel();
}
}
/* add new individual to new generation ****************************/
newGeneration.add(baby);
}
/* finally select best of new Generation *******************************/
newGeneration.selectBest(populationSize);
/* replace current population with the new population ************************/
currentPopulation = newGeneration;
/* Print best indivual every n times *********************************/
if (Config.getPrintEachTimes() != 0 && currentGeneration % Config.getPrintEachTimes() == 0) {
System.out.println(currentPopulation.getBestIndividual());
}
} // End for
status.setTimeStopped(Calendar.getInstance());
stopTime = System.currentTimeMillis();
/* return best Individual *************************************************/
if (Config.isReturnOnlyValidIndividuals()) {
return currentPopulation.getBestValidIndividual();
} else {
return currentPopulation.getBestIndividual();
}
}
