/** Start the background thread. */
public void start() {
// create a random list of cities
cities = new City[TravelingSalesman.CITY_COUNT];
for (int i = 0; i < TravelingSalesman.CITY_COUNT; i++) {
cities[i] =
new City(
(int) (Math.random() * (getBounds().width - 10)),
(int) (Math.random() * (getBounds().height - 60)));
}
// create the initial chromosomes
chromosomes = new Chromosome[TravelingSalesman.POPULATION_SIZE];
for (int i = 0; i < TravelingSalesman.POPULATION_SIZE; i++) {
chromosomes[i] = new Chromosome(cities);
chromosomes[i].setCut(cutLength);
chromosomes[i].setMutation(TravelingSalesman.MUTATION_PERCENT);
}
Chromosome.sortChromosomes(chromosomes, TravelingSalesman.POPULATION_SIZE);
// start up the background thread
started = true;
map.update(map.getGraphics());
generation = 0;
if (worker != null) worker = null;
worker = new Thread(this);
// worker.setPriority(Thread.MIN_PRIORITY);
worker.start();
}
/** Start the background thread. */
public void start() {
// create a random list of cities
cities = new City[TravelingSalesman.CITY_COUNT];
for (int i = 0; i < TravelingSalesman.CITY_COUNT; i++) {
cities[i] =
new City(
(int) (Math.random() * (getBounds().width - 10)),
(int) (Math.random() * (getBounds().height - 60)));
}
// start up the background thread
started = true;
map.update(map.getGraphics());
if (worker != null) worker = null;
worker = new SimulateAnnealing(this);
worker.setPriority(Thread.MIN_PRIORITY);
worker.start();
}
/** The main loop for the background thread. It is here that most of the work os orchestrated. */
public void run() {
double thisCost = 500.0;
double oldCost = 0.0;
double dcost = 500.0;
int countSame = 0;
map.update(map.getGraphics());
while (countSame < 100) {
generation++;
int ioffset = matingPopulationSize;
int mutated = 0;
// Mate the chromosomes in the favoured population
// with all in the mating population
for (int i = 0; i < favoredPopulationSize; i++) {
Chromosome cmother = chromosomes[i];
// Select partner from the mating population
int father = (int) (0.999999 * Math.random() * (double) matingPopulationSize);
Chromosome cfather = chromosomes[father];
mutated += cmother.mate(cfather, chromosomes[ioffset], chromosomes[ioffset + 1]);
ioffset += 2;
}
// The new generation is in the matingPopulation area
// move them to the correct area for sort.
for (int i = 0; i < matingPopulationSize; i++) {
chromosomes[i] = chromosomes[i + matingPopulationSize];
chromosomes[i].calculateCost(cities);
}
// Now sort the new mating population
Chromosome.sortChromosomes(chromosomes, matingPopulationSize);
double cost = chromosomes[0].getCost();
dcost = Math.abs(cost - thisCost);
thisCost = cost;
double mutationRate = 100.0 * (double) mutated / (double) matingPopulationSize;
NumberFormat nf = NumberFormat.getInstance();
nf.setMinimumFractionDigits(2);
nf.setMinimumFractionDigits(2);
status.setText(
"Generation "
+ generation
+ " Cost "
+ (int) thisCost
+ " Mutated "
+ nf.format(mutationRate)
+ "%");
if ((int) thisCost == (int) oldCost) {
countSame++;
} else {
countSame = 0;
oldCost = thisCost;
}
map.update(map.getGraphics());
}
status.setText("Solution found after " + generation + " generations.");
}
public void paint() {
map.update(getGraphics());
}