Esempio n. 1
0
  /**
   * Assigns crowding distances to all solutions in a <code>SolutionSet</code>.
   *
   * @param solutionSet The <code>SolutionSet</code>.
   * @param nObjs Number of objectives.
   */
  public void crowdingDistanceAssignment(SolutionSet solutionSet, int nObjs) {
    int size = solutionSet.size();

    if (size == 0) return;

    if (size == 1) {
      solutionSet.get(0).setCrowdingDistance(Double.POSITIVE_INFINITY);
      return;
    } // if

    if (size == 2) {
      solutionSet.get(0).setCrowdingDistance(Double.POSITIVE_INFINITY);
      solutionSet.get(1).setCrowdingDistance(Double.POSITIVE_INFINITY);
      return;
    } // if

    // Use a new SolutionSet to evite alter original solutionSet
    SolutionSet front = new SolutionSet(size);
    for (int i = 0; i < size; i++) {
      front.add(solutionSet.get(i));
    }

    for (int i = 0; i < size; i++) front.get(i).setCrowdingDistance(0.0);

    double objetiveMaxn;
    double objetiveMinn;
    double distance;

    for (int i = 0; i < nObjs; i++) {
      // Sort the population by Obj n
      front.sort(new ObjectiveComparator(i));
      objetiveMinn = front.get(0).getObjective(i);
      objetiveMaxn = front.get(front.size() - 1).getObjective(i);

      // Set de crowding distance
      front.get(0).setCrowdingDistance(Double.POSITIVE_INFINITY);
      front.get(size - 1).setCrowdingDistance(Double.POSITIVE_INFINITY);

      for (int j = 1; j < size - 1; j++) {
        distance = front.get(j + 1).getObjective(i) - front.get(j - 1).getObjective(i);
        distance = distance / (objetiveMaxn - objetiveMinn);
        distance += front.get(j).getCrowdingDistance();
        front.get(j).setCrowdingDistance(distance);
      } // for
    } // for
  } // crowdingDistanceAssing
Esempio n. 2
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 /**
  * Execute the ElitistES algorithm
 * @throws JMException 
  */
  public SolutionSet execute() throws JMException, ClassNotFoundException {
    int maxEvaluations ;
    int evaluations    ;

    SolutionSet population          ;
    SolutionSet offspringPopulation ;  

    Operator   mutationOperator ;
    Comparator comparator       ;
    
    comparator = new ObjectiveComparator(0) ; // Single objective comparator
    
    // Read the params
    maxEvaluations = ((Integer)this.getInputParameter("maxEvaluations")).intValue();                
   
    // Initialize the variables
    population          = new SolutionSet(mu_) ;   
    offspringPopulation = new SolutionSet(mu_ + lambda_) ;
    
    evaluations  = 0;                

    // Read the operators
    mutationOperator  = this.operators_.get("mutation");

    System.out.println("(" + mu_ + " + " + lambda_+")ES") ;
     
    // Create the parent population of mu solutions
    Solution newIndividual;
    for (int i = 0; i < mu_; i++) {
      newIndividual = new Solution(problem_);                    
      evaluations++;
      population.add(newIndividual);
    } //for
    
    finish {
	// 
    	async {
    		for (int i = 0; i < mu_; i++) {
    			evaluate_internal(population, i, null);
    		}
    	}
    }
     
    // Main loop
    int offsprings ;
    offsprings = lambda_ / mu_ ; 
    while (evaluations < maxEvaluations) {
      // STEP 1. Generate the mu+lambda population
      for (int i = 0; i < mu_; i++) {
        for (int j = 0; j < offsprings; j++) {
          Solution offspring = new Solution(population.get(i)) ;
          offspringPopulation.add(offspring) ;
          evaluations ++ ;
        } // for
      } // for
      
      finish {
	// 
      	async {
      		for (int i = 0; i < mu_*offsprings; i++) {
      			evaluate_internal(offspringPopulation, i, mutationOperator);
      		}
      	}
      }
       
      
      // STEP 2. Add the mu individuals to the offspring population
      for (int i = 0 ; i < mu_; i++) {
        offspringPopulation.add(population.get(i)) ;
      } // for
      population.clear() ;

      // STEP 3. Sort the mu+lambda population
      offspringPopulation.sort(comparator) ;
            
      // STEP 4. Create the new mu population
      for (int i = 0; i < mu_; i++)
        population.add(offspringPopulation.get(i)) ;

      System.out.println("Evaluation: " + evaluations + " Fitness: " + 
          population.get(0).getObjective(0)) ; 

      // STEP 6. Delete the mu+lambda population
      offspringPopulation.clear() ;
    } // while
    
    // Return a population with the best individual
    SolutionSet resultPopulation = new SolutionSet(1) ;
    resultPopulation.add(population.get(0)) ;
    
Esempio n. 3
0
  /**
   * 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
   */
  public SolutionSet execute() throws JMException, ClassNotFoundException {
    int populationSize;
    int maxEvaluations;
    int evaluations;

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

    SolutionSet population;
    SolutionSet offspringPopulation;
    SolutionSet union;

    Distance distance = new Distance();

    // Read the parameters
    populationSize = ((Integer) getInputParameter("populationSize")).intValue();
    maxEvaluations = ((Integer) getInputParameter("maxEvaluations")).intValue();
    indicators = (QualityIndicator) getInputParameter("indicators");

    // Initialize the variables
    population = new SolutionSet(populationSize);
    evaluations = 0;
    requiredEvaluations = 0;

    // Read the operators
    List<Operator> mutationOperators = new ArrayList<Operator>();
    mutationOperators.add(operators_.get("oneNoteMutation"));
    mutationOperators.add(operators_.get("harmonyNoteToPitch"));
    mutationOperators.add(operators_.get("swapHarmonyNotes"));
    mutationOperators.add(operators_.get("melodyNoteToHarmonyNote"));
    mutationOperators.add(operators_.get("pitchSpaceMutation"));
    Operator crossoverOperator = operators_.get("crossover");
    Operator selectionOperator = operators_.get("selection");

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

    int changeCount = 0;

    // Generations ...
    // while ((evaluations < maxEvaluations) && changeCount < 10 *
    // populationSize) {
    while ((evaluations < maxEvaluations)) {
      List<Solution> copySolutions = copyList(population);
      // Create the offSpring solutionSet
      offspringPopulation = new SolutionSet(populationSize);
      Solution[] parents = new Solution[2];
      for (int i = 0; i < (populationSize / 2); i++) {
        if (evaluations < maxEvaluations) {
          // obtain parents
          parents[0] = (Solution) selectionOperator.execute(population);
          parents[1] = (Solution) selectionOperator.execute(population);
          Solution[] offSpring = (Solution[]) crossoverOperator.execute(parents);
          for (Operator operator : mutationOperators) {
            operator.execute(offSpring[0]);
            operator.execute(offSpring[1]);
          }
          if (decorated) {
            decorator.decorate(offSpring[0]);
            decorator.decorate(offSpring[1]);
          }
          problem_.evaluate(offSpring[0]);
          problem_.evaluateConstraints(offSpring[0]);
          problem_.evaluate(offSpring[1]);
          problem_.evaluateConstraints(offSpring[1]);
          offspringPopulation.add(offSpring[0]);
          offspringPopulation.add(offSpring[1]);
          evaluations += 2;
        }
      }

      // Create the solutionSet union of solutionSet and offSpring
      union = ((SolutionSet) population).union(offspringPopulation);

      // Ranking the union
      Ranking ranking = new Ranking(union);

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

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

      while ((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));
        }

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

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

      // 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 jmetal.util.comparators.CrowdingComparator());
        for (int k = 0; k < remain; k++) {
          population.add(front.get(k));
        }

        remain = 0;
      }

      // 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);
        double p = indicators.getTrueParetoFrontHypervolume();
        if (HV >= (0.98 * indicators.getTrueParetoFrontHypervolume())) {
          requiredEvaluations = evaluations;
        }
      }
      // check changes pareto front
      // SolutionSet paretoFront = ranking.getSubfront(0);
      List<Solution> frontSolutions = copyList(population);
      boolean changed = hasPopulationChanged(copySolutions, frontSolutions);
      LOGGER.fine(
          "Population changed: "
              + changed
              + ", evaluations: "
              + evaluations
              + ", change count:"
              + changeCount);
      if (changed) {
        changeCount = 0;
      } else {
        changeCount++;
      }
    }

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

    // Return the first non-dominated front
    Ranking ranking = new Ranking(population);
    return ranking.getSubfront(0);
  }