public Individual run(
EvolutionState state,
SimpleProblemForm p,
Individual x,
int maxIter,
ClusWrapperNonStatic objectClus)
throws Exception { // talvez passe um individuo já com o genoma e score
Random rand = new Random();
// IntegerVectorIndividual mutatedX = (IntegerVectorIndividual) deepClone(x);
IntegerVectorIndividual mutatedX = (IntegerVectorIndividual) x.clone();
mutatedX.evaluated = false;
double alpha = 0.95; // cálculo do alpha, percentual de diminuição da temperatura
double t_final = 1;
double t0 = Math.pow(alpha, maxIter + Math.log(t_final)); // estimar a temperatura inicial
double temp_atual = t0; // tf = temperatura da vez
int iter = 0;
maxIter *= 2;
while (iter < maxIter) {
int[] genome = mutatedX.genome;
genome = mutate(genome, 1 - iter / maxIter); // mutar o vetor
mutatedX.setGenome(genome);
((ec.Problem) p).prepareToEvaluate(state, 0);
System.out.print("Mutated Genoma: " + mutatedX.genome[0]);
for (int i = 0; i < mutatedX.genomeLength(); i++) System.out.print("," + mutatedX.genome[i]);
System.out.println();
p.evaluate(state, mutatedX, 0, 0, objectClus);
((ec.Problem) p).finishEvaluating(state, 0);
double newF = mutatedX.fitness.fitness();
// adequar para minimizar o fitness
float delta = (float) (x.fitness.fitness() - newF);
if (delta < 0 || rand.nextDouble() < Math.exp(-(delta) / temp_atual)) {
((IntegerVectorIndividual) x).setGenome(mutatedX.getGenome());
x.evaluated = false;
((ec.Problem) p).prepareToEvaluate(state, 0);
p.evaluate(state, x, 0, 0, objectClus);
((ec.Problem) p).finishEvaluating(state, 0);
}
iter += 1;
temp_atual = temp_atual * alpha;
}
return x;
}
