public void optimize() {
for (int ant = 0; ant < populationSize; ant++) {
// generate solution
double[] newPoint = new double[dimension];
for (int d = 0; d < dimension; d++)
newPoint[d] = (generator.nextGaussian() * deviations[d]) + means[d];
// improve solution using gradient
if (gradientWeight != 0.0) {
double[] gradient = function.gradientAt(Point.at(newPoint)).toArray();
for (int d = 0; d < dimension; d++) newPoint[d] -= gradientWeight * gradient[d];
}
// get solution error (& update best solution
double error = function.valueAt(Point.at(newPoint));
paths[ant] = ValuePoint.at(Point.at(newPoint), error);
if (error < best.getValue()) best = paths[ant]; // ValuePoint.at(Point.at(newPoint), error);
}
// update pheromone
double[] bestVect = best.getPoint().toArray();
for (int i = 0; i < dimension; i++) {
deviations[i] =
(1 - evaporationFactor) * deviations[i]
+ evaporationFactor * Math.abs(bestVect[i] - means[i]);
means[i] = (1 - evaporationFactor) * means[i] + evaporationFactor * bestVect[i];
}
telemetry = new ValuePointListTelemetry(Arrays.asList(paths));
if (consumer != null) consumer.notifyOf(this);
stopCondition.setValue(best.getValue());
}