Exemple #1
5
  /**
   * Runs the NSGA-II algorithm.
   *
   * @return a <code>SolutionSet</code> that is a set of non dominated solutions as a result of the
   *     algorithm execution
   * @throws JMException
   */
  @Test
  public Poblacion execute() throws JMException, ClassNotFoundException {
    int populationSize;
    int maxEvaluations;
    int evaluations;
    int probMutacion;
    int nrocaso;
    int corridas;

    int requiredEvaluations; // Use in the example of use of the
    // indicators object (see below)

    // Poblacion population;
    // SolutionSet population;
    Poblacion offspringPopulation;
    Poblacion union;
    Poblacion copyPopulation;

    Operator mutationOperator;
    Operator crossoverOperator;
    Operator selectionOperator;

    Distance distance = new Distance();

    // Read the parameters
    populationSize = ((Integer) getInputParameter("populationSize")).intValue();
    maxEvaluations = ((Integer) getInputParameter("maxEvaluations")).intValue();
    probMutacion = ((Integer) getInputParameter("probMutacion")).intValue();
    nrocaso = ((Integer) getInputParameter("nrocaso")).intValue();
    corridas = ((Integer) getInputParameter("corridas")).intValue();

    // Initialize the variables
    // population = new SolutionSet(populationSize);
    evaluations = 0;
    caso = casosDePrueba[nrocaso];

    // Initialize Nsfnet
    NSFNET = em.find(Red.class, 1); // NSFnet
    NSFNET.inicializar();

    long time_start, time_end = 0;
    // captura tiempo Inicial
    time_start = System.currentTimeMillis();
    // 0. Obtener Poblacion Inicial
    this.obtenerPoblacion(populationSize);

    requiredEvaluations = 0;

    // Read the operators
    // mutationOperator = operators_.get("mutation");
    // crossoverOperator = operators_.get("crossover");
    // selectionOperator = operators_.get("selection");
    OperadorSeleccion seleccionOp = new TorneoBinario();

    // Create the initial solutionSet
    Solution newSolution;
    // population = new Poblacion(populationSize);
    for (int i = 0; i < populationSize; i++) {
      newSolution = new Solution(problem_);
      problem_.evaluate(newSolution);
      problem_.evaluateConstraints(newSolution);
      evaluations++;
      population.add(newSolution);
    } // for

    Ranking ranking = new Ranking(population);
    evaluations = 0;
    int cantIt = 0;
    int size, tamanho = 0;
    // Generations

    System.out.println(caso + "-" + corridas + " Test Genetico.");

    while (evaluations < tiempoTotal[nrocaso]) {

      size = population.getIndividuos().size();
      tamanho = (size * size) + size;
      offspringPopulation = new Poblacion(populationSize * populationSize + populationSize);
      copyPopulation = new Poblacion(populationSize * populationSize + populationSize);
      // offspringPopulation.copiarPoblacion(population);;

      // for (int i = 0; i < (populationSize); i++) {
      if (evaluations < tiempoTotal[nrocaso]) {

        for (Individuo ind : population.getIndividuos()) {
          Solution s = (Solution) ind;
          s.setNumberOfObjectives(problem_.getNumberOfObjectives());
          problem_.evaluate(s);
          // if (s.getCosto()<2.8)
          // System.out.println(s.getCosto());
          if (s.getCosto() > 0) offspringPopulation.add(s);
        }

        Collection<Individuo> selectos = seleccionOp.seleccionar(population);

        population.cruzar(selectos, probMutacion);
        /*copyPopulation.copiarPoblacion(offspringPopulation);
        Poblacion mejores = getFront(copyPopulation);
        population.siguienteGeneracion(mejores);*/
        population.siguienteGeneracion();

        evaluations++;
      } // if

      // } // for

      for (Individuo ind : population.getIndividuos()) {
        Solution s = (Solution) ind;
        s.setNumberOfObjectives(problem_.getNumberOfObjectives());
        problem_.evaluate(s);
        if (s.getCosto() > 0) offspringPopulation.add(s);
      }
      // Create the solutionSet union of solutionSet and offSpring
      // union = ((SolutionSet) population).union(offspringPopulation);

      union = offspringPopulation.union(population);
      // Ranking the union
      ranking = new Ranking(union);

      int remain = populationSize;
      int index = 0;
      Poblacion front = null;
      population.clear();

      // Obtain the next front
      front = ranking.getSubfront(index);

      while (front != null && (remain > 0) && (remain >= front.size())) {
        // Assign crowding distance to individuals
        distance.crowdingDistanceAssignment(front, problem_.getNumberOfObjectives());
        // Add the individuals of this front
        for (int k = 0; k < front.size(); k++) {
          population.add(front.get(k));
        } // for

        // Decrement remain
        remain = remain - front.size();

        // Obtain the next front
        index++;
        if (remain > 0) {
          front = ranking.getSubfront(index);
        } // if
      } // while

      if (front != null) {
        // Remain is less than front(index).size, insert only the best one
        if (remain > 0) { // front contains individuals to insert
          distance.crowdingDistanceAssignment(front, problem_.getNumberOfObjectives());
          front.sort(new CrowdingComparator());
          for (int k = 0; k < remain; k++) {
            population.add(front.get(k));
          } // for
        }
        remain = 0;
      } // if

      // This piece of code shows how to use the indicator object into the code
      // of NSGA-II. In particular, it finds the number of evaluations required
      // by the algorithm to obtain a Pareto front with a hypervolume higher
      // than the hypervolume of the true Pareto front.
      /*if ((indicators != null) &&
          (requiredEvaluations == 0)) {
        double HV = indicators.getHypervolume(population);
        if (HV >= (0.98 * indicators.getTrueParetoFrontHypervolume())) {
          requiredEvaluations = evaluations;
        } // if
      } // if*/

      if (evaluations % maxEvaluations == 0) {
        System.out.println();
        System.out.print("Población Nro: " + evaluations + " ");
        // System.out.println("MEJOR--> " + p.getMejor().toString());
        System.out.print("Costo-MEJOR==> ");
        ranking = new Ranking(population);
        if (ranking.getSubfront(0) != null) {
          ranking.getSubfront(0).printParcialResults();
          ranking.getSubfront(0).printVariablesToFile("VAR_p3" + "_" + caso);
        }
        // ((Solution) p.getMejor()).imprimirCosto();
      }
    } // while
    cantIt++;

    // captura tiempo final
    // time_end = System.currentTimeMillis();
    // Calculo del Tiempo
    /*long tiempo = time_end - time_start;
    long hora = tiempo / 3600000;
    long restohora = tiempo % 3600000;
    long minuto = restohora / 60000;
    long restominuto = restohora % 60000;
    long segundo = restominuto / 1000;
    long restosegundo = restominuto % 1000;
    String time = hora + ":" + minuto + ":" + segundo + "." + restosegundo;
    time = " Tiempo: " + time;
    String fin = caso + " FIN - Test Genetico. Tiempo:" + time;
    fin += " - Nº Generaciones: " + evaluations;
    System.out.println(fin);
    */
    System.out.println("Evaluaciones: " + evaluations);

    // Return as output parameter the required evaluations
    setOutputParameter("evaluations", requiredEvaluations);

    // Return the first non-dominated front
    ranking = new Ranking(population);
    if (ranking.getSubfront(0) != null) ranking.getSubfront(0).printFeasibleFUN("FUN_NSGAII");

    return ranking.getSubfront(0);
  } // execute
  /**
   * Creates a new instance of problem CEC2009_UF6.
   *
   * @param numberOfVariables Number of variables.
   * @param solutionType The solution type must "Real" or "BinaryReal".
   */
  public CEC2009_UF6(String solutionType, Integer numberOfVariables, int N, double epsilon)
      throws ClassNotFoundException {
    numberOfVariables_ = numberOfVariables.intValue();
    numberOfObjectives_ = 2;
    numberOfConstraints_ = 0;
    problemName_ = "CEC2009_UF6";

    N_ = N;
    epsilon_ = epsilon;

    upperLimit_ = new double[numberOfVariables_];
    lowerLimit_ = new double[numberOfVariables_];

    lowerLimit_[0] = 0.0;
    upperLimit_[0] = 1.0;
    for (int var = 1; var < numberOfVariables_; var++) {
      lowerLimit_[var] = -1.0;
      upperLimit_[var] = 1.0;
    } // for

    if (solutionType.compareTo("BinaryReal") == 0) solutionType_ = new BinaryRealSolutionType(this);
    else if (solutionType.compareTo("Real") == 0) solutionType_ = new RealSolutionType(this);
    else {
      System.out.println("Error: solution type " + solutionType + " invalid");
      System.exit(-1);
    }
  } // CEC2009_UF6
Exemple #3
0
  public boolean[] randomProduct() {

    boolean[] prod = new boolean[ProductLineProblem.numFeatures];
    for (int i = 0; i < prod.length; i++) {
      prod[i] = r.nextBoolean();
    }

    int rand = r.nextInt(3);

    try {
      IOrder order;
      if (rand == 0) {
        order =
            new RandomWalkDecorator(new VarOrderHeap(new NegativeLiteralSelectionStrategy()), 1);
      } else if (rand == 1) {
        order =
            new RandomWalkDecorator(new VarOrderHeap(new PositiveLiteralSelectionStrategy()), 1);
      } else {
        order = new RandomWalkDecorator(new VarOrderHeap(new RandomLiteralSelectionStrategy()), 1);
      }

      // dimacsSolver.reset();
      ISolver dimacsSolver2 = SolverFactory.instance().createSolverByName("MiniSAT");
      dimacsSolver2.setTimeout(SATtimeout);

      DimacsReader dr = new DimacsReader(dimacsSolver2);
      dr.parseInstance(new FileReader(ProductLineProblem.fm));
      ((Solver) dimacsSolver2).setOrder(order);

      ISolver solverIterator = new ModelIterator(dimacsSolver2);
      solverIterator.setTimeoutMs(iteratorTimeout);

      if (solverIterator.isSatisfiable()) {
        int[] i = solverIterator.findModel();

        for (int j = 0; j < i.length; j++) {
          int feat = i[j];

          int posFeat = feat > 0 ? feat : -feat;

          if (posFeat > 0) {
            prod[posFeat - 1] = feat > 0;
          }

          //                    else
          //                    {
          //                         prod[nFeat-1] = r.nextBoolean();
          //                    }
        }
      }

      // solverIterator = null;
    } catch (Exception e) {
      e.printStackTrace();
      System.exit(0);
    }

    return prod;
  }
Exemple #4
0
  /** @param args the command line arguments -- modelname, alg_name, evaluation_times, [runid] */
  public static void main(String[] args) throws Exception {

    try {
      String name = args[0];
      URL location = Main.class.getProtectionDomain().getCodeSource().getLocation();
      String loc = location.toString();
      String project_path =
          loc.substring(5, loc.lastIndexOf("SPL/")) + "SPL/"; // with '/' at the end
      String fm = project_path + "dimacs_data/" + name + ".dimacs";
      String augment = fm + ".augment";
      String dead = fm + ".dead";
      String mandatory = fm + ".mandatory";
      String seed = fm + ".richseed";
      String opfile = fm + ".sipop";

      Problem p = new ProductLineProblem(fm, augment, mandatory, dead, seed);
      //            Problem p = new ProductLineProblemNovelPrep(fm, augment, mandatory, dead, seed,
      // opfile);
      //            GroupedProblem.grouping((ProductLineProblem) p, 100); System.exit(0);
      Algorithm a;
      int evaluation_times = Integer.parseInt(args[2]);
      String alg_name = args[1];
      String runid = "";
      if (args.length >= 4) {
        runid = args[3];
      }

      switch (alg_name) {
        case "IBEA":
          a = new SPL_SettingsIBEA(p).configureICSE2013(evaluation_times);
          break;
        case "SIPIBEA":
          a = new SPL_SettingsIBEA(p).configureSIPIBEA(evaluation_times);
          break;
        case "SPEA2":
          a = new SPL_SettingsEMOs(p).configureSPEA2(evaluation_times);
          break;
        case "NSGA2":
          a = new SPL_SettingsEMOs(p).configureNSGA2(evaluation_times);
          break;
        case "IBEASEED":
          a = new SPL_SettingsIBEA(p).configureIBEASEED(evaluation_times);
          break;
        case "SATIBEA":
          // a = new SPL_SettingsIBEA(p).configureICSE15(1000, fm, ((ProductLineProblem)
          // p).getNumFeatures(),
          // ((ProductLineProblem) p).getConstraints());
          a =
              new SPL_SettingsIBEA(p)
                  .configureSATIBEA(
                      evaluation_times,
                      fm,
                      ((ProductLineProblem) p).getNumFeatures(),
                      ((ProductLineProblem) p).getConstraints());
          break;
        default:
          a = new SPL_SettingsIBEA(p).configureICSE2013(evaluation_times);
      }

      long start = System.currentTimeMillis();
      SolutionSet pop = a.execute();
      float total_time = (System.currentTimeMillis() - start) / 1000.0f;

      String file_tag =
          name + "_" + alg_name + '_' + evaluation_times / 1000 + "k_" + runid + ".txt";
      String file_path = project_path + "j_res/" + file_tag;
      File file = new File(file_path);

      if (!file.exists()) {
        file.createNewFile();
      }

      FileWriter fw = new FileWriter(file.getAbsoluteFile());
      BufferedWriter bw = new BufferedWriter(fw);

      for (int i = 0; i < pop.size(); i++) {
        Variable v = pop.get(i).getDecisionVariables()[0];
        bw.write((Binary) v + "\n");
        System.out.println("Conf" + (i + 1) + ": " + (Binary) v + " ");
      }

      bw.write("~~~\n");

      for (int i = 0; i < pop.size(); i++) {
        Variable v = pop.get(i).getDecisionVariables()[0];
        for (int j = 0; j < pop.get(i).getNumberOfObjectives(); j++) {
          bw.write(pop.get(i).getObjective(j) + " ");
          System.out.print(pop.get(i).getObjective(j) + " ");
        }
        bw.write("\n");
        System.out.println("");
      }

      System.out.println(total_time);
      bw.write("~~~\n" + total_time + "\n");

      bw.close();
      fw.close();

    } catch (Exception e) {
      e.printStackTrace();
    }
  }