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; }