/**
   * Returns a new {@link MSOPS} instance.
   *
   * @param properties the properties for customizing the new {@code MSOPS} instance
   * @param problem the problem
   * @return a new {@code MSOPS} instance
   */
  private Algorithm newMSOPS(TypedProperties properties, Problem problem) {
    if (!checkType(RealVariable.class, problem)) {
      throw new FrameworkException("unsupported decision variable type");
    }

    int populationSize = (int) properties.getDouble("populationSize", 100);
    int numberOfWeights = (int) properties.getDouble("numberOfWeights", 50);

    Initialization initialization = new RandomInitialization(problem, populationSize);

    List<double[]> weights =
        new RandomGenerator(problem.getNumberOfObjectives(), numberOfWeights).generate();

    // normalize weights so their magnitude is 1
    for (int i = 0; i < weights.size(); i++) {
      weights.set(i, Vector.normalize(weights.get(i)));
    }

    MSOPSRankedPopulation population = new MSOPSRankedPopulation(weights);

    DifferentialEvolutionSelection selection = new DifferentialEvolutionSelection();

    DifferentialEvolutionVariation variation =
        (DifferentialEvolutionVariation)
            OperatorFactory.getInstance().getVariation("de", properties, problem);

    return new MSOPS(problem, population, selection, variation, initialization);
  }
  /**
   * Returns a new {@link MOEAD} instance. Only real encodings are supported.
   *
   * @param properties the properties for customizing the new {@code MOEAD} instance
   * @param problem the problem
   * @return a new {@code MOEAD} instance
   * @throws FrameworkException if the decision variables are not real valued
   */
  private Algorithm newMOEAD(TypedProperties properties, Problem problem) {
    if (!checkType(RealVariable.class, problem)) {
      throw new FrameworkException("unsupported decision variable type");
    }

    int populationSize = (int) properties.getDouble("populationSize", 100);

    // enforce population size lower bound
    if (populationSize < problem.getNumberOfObjectives()) {
      System.err.println("increasing MOEA/D population size");
      populationSize = problem.getNumberOfObjectives();
    }

    Initialization initialization = new RandomInitialization(problem, populationSize);

    Variation variation = OperatorFactory.getInstance().getVariation("de+pm", properties, problem);

    int neighborhoodSize = 20;
    int eta = 2;

    if (properties.contains("neighborhoodSize")) {
      neighborhoodSize =
          Math.max(2, (int) (properties.getDouble("neighborhoodSize", 0.1) * populationSize));
    }

    if (neighborhoodSize > populationSize) {
      neighborhoodSize = populationSize;
    }

    if (properties.contains("eta")) {
      eta = Math.max(2, (int) (properties.getDouble("eta", 0.01) * populationSize));
    }

    MOEAD algorithm =
        new MOEAD(
            problem,
            neighborhoodSize,
            initialization,
            variation,
            properties.getDouble("delta", 0.9),
            eta,
            (int) properties.getDouble("updateUtility", -1));

    return algorithm;
  }
Пример #3
0
  /**
   * Returns the distance in objective space between the two solutions.
   *
   * @param problem the problem
   * @param a the first solution
   * @param b the second solution
   * @param power the power ({@code 1.0} for Manhattan distance, {@code 2.0} for Euclidean distance)
   * @return the distance in objective space between the two solutions
   */
  private static double distance(Problem problem, Solution a, Solution b, double power) {
    double distance = 0.0;

    for (int i = 0; i < problem.getNumberOfObjectives(); i++) {
      distance += Math.pow(Math.abs(a.getObjective(i) - b.getObjective(i)), power);
    }

    return Math.pow(distance, 1.0 / power);
  }
  /**
   * Returns {@code true} if all decision variables are assignment-compatible with the specified
   * type; {@code false} otherwise.
   *
   * @param type the type of decision variable
   * @param problem the problem
   * @return {@code true} if all decision variables are assignment-compatible with the specified
   *     type; {@code false} otherwise
   */
  private boolean checkType(Class<? extends Variable> type, Problem problem) {
    Solution solution = problem.newSolution();

    for (int i = 0; i < solution.getNumberOfVariables(); i++) {
      if (!type.isInstance(solution.getVariable(i))) {
        return false;
      }
    }

    return true;
  }
  /**
   * Returns a new {@link SMPSO} instance.
   *
   * @param properties the properties for customizing the new {@code SMPSO} instance
   * @param problem the problem
   * @return a new {@code SMPSO} instance
   */
  private Algorithm newSMPSO(TypedProperties properties, Problem problem) {
    if (!checkType(RealVariable.class, problem)) {
      throw new FrameworkException("unsupported decision variable type");
    }

    int populationSize = (int) properties.getDouble("populationSize", 100);
    int archiveSize = (int) properties.getDouble("archiveSize", 100);
    double mutationProbability =
        properties.getDouble("pm.rate", 1.0 / problem.getNumberOfVariables());
    double distributionIndex = properties.getDouble("pm.distributionIndex", 20.0);

    return new SMPSO(problem, populationSize, archiveSize, mutationProbability, distributionIndex);
  }
  /**
   * Returns a new {@link CMAES} instance.
   *
   * @param properties the properties for customizing the new {@code CMAES} instance
   * @param problem the problem
   * @return a new {@code CMAES} instance
   */
  private Algorithm newCMAES(TypedProperties properties, Problem problem) {
    if (!checkType(RealVariable.class, problem)) {
      throw new FrameworkException("unsupported decision variable type");
    }

    int lambda = (int) properties.getDouble("lambda", 100);
    double cc = properties.getDouble("cc", -1.0);
    double cs = properties.getDouble("cs", -1.0);
    double damps = properties.getDouble("damps", -1.0);
    double ccov = properties.getDouble("ccov", -1.0);
    double ccovsep = properties.getDouble("ccovsep", -1.0);
    double sigma = properties.getDouble("sigma", -1.0);
    int diagonalIterations = (int) properties.getDouble("diagonalIterations", 0);
    String indicator = properties.getString("indicator", "crowding");
    double[] initialSearchPoint = properties.getDoubleArray("initialSearchPoint", null);
    NondominatedPopulation archive = null;
    FitnessEvaluator fitnessEvaluator = null;

    if (problem.getNumberOfObjectives() == 1) {
      archive = new NondominatedPopulation();
    } else {
      archive =
          new EpsilonBoxDominanceArchive(
              properties.getDoubleArray(
                  "epsilon", new double[] {EpsilonHelper.getEpsilon(problem)}));
    }

    if ("hypervolume".equals(indicator)) {
      fitnessEvaluator = new HypervolumeFitnessEvaluator(problem);
    } else if ("epsilon".equals(indicator)) {
      fitnessEvaluator = new AdditiveEpsilonIndicatorFitnessEvaluator(problem);
    }

    CMAES cmaes =
        new CMAES(
            problem,
            lambda,
            fitnessEvaluator,
            archive,
            initialSearchPoint,
            false,
            cc,
            cs,
            damps,
            ccov,
            ccovsep,
            sigma,
            diagonalIterations);

    return cmaes;
  }
Пример #7
0
  @Override
  public Solution[] initialize() {
    Solution[] initialPopulation = new Solution[populationSize];

    for (int i = 0; i < populationSize; i++) {
      Solution solution = problem.newSolution();

      for (int j = 0; j < solution.getNumberOfVariables(); j++) {
        solution.getVariable(j).randomize();
      }

      initialPopulation[i] = solution;
    }

    return initialPopulation;
  }
  /**
   * Returns a new {@link OMOPSO} instance.
   *
   * @param properties the properties for customizing the new {@code OMOPSO} instance
   * @param problem the problem
   * @return a new {@code OMOPSO} instance
   */
  private Algorithm newOMOPSO(TypedProperties properties, Problem problem) {
    if (!checkType(RealVariable.class, problem)) {
      throw new FrameworkException("unsupported decision variable type");
    }

    int populationSize = (int) properties.getDouble("populationSize", 100);
    int archiveSize = (int) properties.getDouble("archiveSize", 100);
    int maxIterations = (int) properties.getDouble("maxEvaluations", 25000) / populationSize;
    double mutationProbability =
        properties.getDouble("mutationProbability", 1.0 / problem.getNumberOfVariables());
    double perturbationIndex = properties.getDouble("perturbationIndex", 0.5);
    double[] epsilon =
        properties.getDoubleArray("epsilon", new double[] {EpsilonHelper.getEpsilon(problem)});

    return new OMOPSO(
        problem,
        populationSize,
        archiveSize,
        epsilon,
        mutationProbability,
        perturbationIndex,
        maxIterations);
  }
  /**
   * Returns a new {@link IBEA} instance.
   *
   * @param properties the properties for customizing the new {@code IBEA} instance
   * @param problem the problem
   * @return a new {@code IBEA} instance
   */
  private Algorithm newIBEA(TypedProperties properties, Problem problem) {
    if (problem.getNumberOfConstraints() > 0) {
      throw new ProviderNotFoundException(
          "IBEA", new ProviderLookupException("constraints not supported"));
    }

    int populationSize = (int) properties.getDouble("populationSize", 100);
    String indicator = properties.getString("indicator", "hypervolume");
    IndicatorFitnessEvaluator fitnessEvaluator = null;

    Initialization initialization = new RandomInitialization(problem, populationSize);

    Variation variation = OperatorFactory.getInstance().getVariation(null, properties, problem);

    if ("hypervolume".equals(indicator)) {
      fitnessEvaluator = new HypervolumeFitnessEvaluator(problem);
    } else if ("epsilon".equals(indicator)) {
      fitnessEvaluator = new AdditiveEpsilonIndicatorFitnessEvaluator(problem);
    } else {
      throw new IllegalArgumentException("invalid indicator: " + indicator);
    }

    return new IBEA(problem, null, initialization, variation, fitnessEvaluator);
  }
  /**
   * Returns a new {@link RVEA} instance.
   *
   * @param properties the properties for customizing the new {@code RVEA} instance
   * @param problem the problem
   * @return a new {@code RVEA} instance
   */
  private Algorithm newRVEA(TypedProperties properties, Problem problem) {
    int divisionsOuter = 4;
    int divisionsInner = 0;

    if (problem.getNumberOfObjectives() < 2) {
      throw new FrameworkException("RVEA requires at least two objectives");
    }

    if (properties.contains("divisionsOuter") && properties.contains("divisionsInner")) {
      divisionsOuter = (int) properties.getDouble("divisionsOuter", 4);
      divisionsInner = (int) properties.getDouble("divisionsInner", 0);
    } else if (properties.contains("divisions")) {
      divisionsOuter = (int) properties.getDouble("divisions", 4);
    } else if (problem.getNumberOfObjectives() == 1) {
      divisionsOuter = 100;
    } else if (problem.getNumberOfObjectives() == 2) {
      divisionsOuter = 99;
    } else if (problem.getNumberOfObjectives() == 3) {
      divisionsOuter = 12;
    } else if (problem.getNumberOfObjectives() == 4) {
      divisionsOuter = 8;
    } else if (problem.getNumberOfObjectives() == 5) {
      divisionsOuter = 6;
    } else if (problem.getNumberOfObjectives() == 6) {
      divisionsOuter = 4;
      divisionsInner = 1;
    } else if (problem.getNumberOfObjectives() == 7) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else if (problem.getNumberOfObjectives() == 8) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else if (problem.getNumberOfObjectives() == 9) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else if (problem.getNumberOfObjectives() == 10) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else {
      divisionsOuter = 2;
      divisionsInner = 1;
    }

    // compute number of reference vectors
    int populationSize =
        (int)
            (CombinatoricsUtils.binomialCoefficient(
                    problem.getNumberOfObjectives() + divisionsOuter - 1, divisionsOuter)
                + (divisionsInner == 0
                    ? 0
                    : CombinatoricsUtils.binomialCoefficient(
                        problem.getNumberOfObjectives() + divisionsInner - 1, divisionsInner)));

    Initialization initialization = new RandomInitialization(problem, populationSize);

    ReferenceVectorGuidedPopulation population =
        new ReferenceVectorGuidedPopulation(
            problem.getNumberOfObjectives(),
            divisionsOuter,
            divisionsInner,
            properties.getDouble("alpha", 2.0));

    if (!properties.contains("sbx.swap")) {
      properties.setBoolean("sbx.swap", false);
    }

    if (!properties.contains("sbx.distributionIndex")) {
      properties.setDouble("sbx.distributionIndex", 30.0);
    }

    if (!properties.contains("pm.distributionIndex")) {
      properties.setDouble("pm.distributionIndex", 20.0);
    }

    Variation variation = OperatorFactory.getInstance().getVariation(null, properties, problem);

    int maxGenerations = (int) (properties.getDouble("maxEvaluations", 10000) / populationSize);
    int adaptFrequency = (int) properties.getDouble("adaptFrequency", maxGenerations / 10);

    return new RVEA(problem, population, variation, initialization, maxGenerations, adaptFrequency);
  }
  /**
   * Returns a new {@link DBEA} instance.
   *
   * @param properties the properties for customizing the new {@code DBEA} instance
   * @param problem the problem
   * @return a new {@code DBEA} instance
   */
  private Algorithm newDBEA(TypedProperties properties, Problem problem) {
    int divisionsOuter = 4;
    int divisionsInner = 0;

    if (properties.contains("divisionsOuter") && properties.contains("divisionsInner")) {
      divisionsOuter = (int) properties.getDouble("divisionsOuter", 4);
      divisionsInner = (int) properties.getDouble("divisionsInner", 0);
    } else if (properties.contains("divisions")) {
      divisionsOuter = (int) properties.getDouble("divisions", 4);
    } else if (problem.getNumberOfObjectives() == 1) {
      divisionsOuter = 100;
    } else if (problem.getNumberOfObjectives() == 2) {
      divisionsOuter = 99;
    } else if (problem.getNumberOfObjectives() == 3) {
      divisionsOuter = 12;
    } else if (problem.getNumberOfObjectives() == 4) {
      divisionsOuter = 8;
    } else if (problem.getNumberOfObjectives() == 5) {
      divisionsOuter = 6;
    } else if (problem.getNumberOfObjectives() == 6) {
      divisionsOuter = 4;
      divisionsInner = 1;
    } else if (problem.getNumberOfObjectives() == 7) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else if (problem.getNumberOfObjectives() == 8) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else if (problem.getNumberOfObjectives() == 9) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else if (problem.getNumberOfObjectives() == 10) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else {
      divisionsOuter = 2;
      divisionsInner = 1;
    }

    int populationSize =
        (int)
            (CombinatoricsUtils.binomialCoefficient(
                    problem.getNumberOfObjectives() + divisionsOuter - 1, divisionsOuter)
                + (divisionsInner == 0
                    ? 0
                    : CombinatoricsUtils.binomialCoefficient(
                        problem.getNumberOfObjectives() + divisionsInner - 1, divisionsInner)));

    Initialization initialization = new RandomInitialization(problem, populationSize);

    Variation variation = OperatorFactory.getInstance().getVariation(null, properties, problem);

    return new DBEA(problem, initialization, variation, divisionsOuter, divisionsInner);
  }
  /**
   * Returns a new {@link NSGAIII} instance.
   *
   * @param properties the properties for customizing the new {@code NSGAIII} instance
   * @param problem the problem
   * @return a new {@code NSGAIII} instance
   */
  private Algorithm newNSGAIII(TypedProperties properties, Problem problem) {
    int divisionsOuter = 4;
    int divisionsInner = 0;

    if (properties.contains("divisionsOuter") && properties.contains("divisionsInner")) {
      divisionsOuter = (int) properties.getDouble("divisionsOuter", 4);
      divisionsInner = (int) properties.getDouble("divisionsInner", 0);
    } else if (properties.contains("divisions")) {
      divisionsOuter = (int) properties.getDouble("divisions", 4);
    } else if (problem.getNumberOfObjectives() == 1) {
      divisionsOuter = 100;
    } else if (problem.getNumberOfObjectives() == 2) {
      divisionsOuter = 99;
    } else if (problem.getNumberOfObjectives() == 3) {
      divisionsOuter = 12;
    } else if (problem.getNumberOfObjectives() == 4) {
      divisionsOuter = 8;
    } else if (problem.getNumberOfObjectives() == 5) {
      divisionsOuter = 6;
    } else if (problem.getNumberOfObjectives() == 6) {
      divisionsOuter = 4;
      divisionsInner = 1;
    } else if (problem.getNumberOfObjectives() == 7) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else if (problem.getNumberOfObjectives() == 8) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else if (problem.getNumberOfObjectives() == 9) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else if (problem.getNumberOfObjectives() == 10) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else {
      divisionsOuter = 2;
      divisionsInner = 1;
    }

    int populationSize;

    if (properties.contains("populationSize")) {
      populationSize = (int) properties.getDouble("populationSize", 100);
    } else {
      // compute number of reference points
      populationSize =
          (int)
              (CombinatoricsUtils.binomialCoefficient(
                      problem.getNumberOfObjectives() + divisionsOuter - 1, divisionsOuter)
                  + (divisionsInner == 0
                      ? 0
                      : CombinatoricsUtils.binomialCoefficient(
                          problem.getNumberOfObjectives() + divisionsInner - 1, divisionsInner)));

      // round up to a multiple of 4
      populationSize = (int) Math.ceil(populationSize / 4d) * 4;
    }

    Initialization initialization = new RandomInitialization(problem, populationSize);

    ReferencePointNondominatedSortingPopulation population =
        new ReferencePointNondominatedSortingPopulation(
            problem.getNumberOfObjectives(), divisionsOuter, divisionsInner);

    Selection selection = null;

    if (problem.getNumberOfConstraints() == 0) {
      selection =
          new Selection() {

            @Override
            public Solution[] select(int arity, Population population) {
              Solution[] result = new Solution[arity];

              for (int i = 0; i < arity; i++) {
                result[i] = population.get(PRNG.nextInt(population.size()));
              }

              return result;
            }
          };
    } else {
      selection =
          new TournamentSelection(
              2,
              new ChainedComparator(
                  new AggregateConstraintComparator(),
                  new DominanceComparator() {

                    @Override
                    public int compare(Solution solution1, Solution solution2) {
                      return PRNG.nextBoolean() ? -1 : 1;
                    }
                  }));
    }

    // disable swapping variables in SBX operator to remain consistent with
    // Deb's implementation (thanks to Haitham Seada for identifying this
    // discrepancy)
    if (!properties.contains("sbx.swap")) {
      properties.setBoolean("sbx.swap", false);
    }

    if (!properties.contains("sbx.distributionIndex")) {
      properties.setDouble("sbx.distributionIndex", 30.0);
    }

    if (!properties.contains("pm.distributionIndex")) {
      properties.setDouble("pm.distributionIndex", 20.0);
    }

    Variation variation = OperatorFactory.getInstance().getVariation(null, properties, problem);

    return new NSGAII(problem, population, null, selection, variation, initialization);
  }