Package org.moeaframework.problem.DTLZ

Class DTLZ6

java.lang.Object
org.moeaframework.problem.AbstractProblem
org.moeaframework.problem.DTLZ.DTLZ
org.moeaframework.problem.DTLZ.DTLZ6
All Implemented Interfaces:
AutoCloseable, Problem, AnalyticalProblem
public class DTLZ6 extends DTLZ implements AnalyticalProblem
The DTLZ6 test problem.

Note: The original DTLZ5 and DTLZ6 problems have a known defect where they produce additional solutions than expected. See Deb and Saxena (2006) for more details.

References:

  1. Deb, K. and Saxena, D. K. (2006). Searching for Pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems. In The 2006 IEEE Congress on Evolutionary Computation, pages 3353–3360.

  • Constructor Details

    • DTLZ6

      public DTLZ6(int numberOfObjectives)
      Constructs a DTLZ6 test problem with the specified number of objectives. This is equivalent to calling new DTLZ6(numberOfObjectives+9, numberOfObjectives).
      Parameters:
      numberOfObjectives - the number of objectives for this problem

    • DTLZ6

      public DTLZ6(int numberOfVariables, int numberOfObjectives)
      Constructs a DTLZ6 test problem with the specified number of variables and objectives.
      Parameters:
      numberOfVariables - the number of variables for this problem
      numberOfObjectives - the number of objectives for this problem

  • Method Details

    • evaluate

      public void evaluate(Solution solution)
      Description copied from interface: Problem
      Evaluates the solution, updating the solution's objectives in place. Algorithms must explicitly call this method when appropriate to evaluate new solutions or reevaluate modified solutions.
      Specified by:
      evaluate in interface Problem
      Parameters:
      solution - the solution to be evaluated

    • generate

      public Solution generate()
      Description copied from interface: AnalyticalProblem
      Returns a randomly-generated solution using the analytical solution to this problem. The exact behavior of this method depends on the implementation, but in general (1) the solutions should be non-dominated and (2) spread uniformly across the Pareto front.

      It is not always possible to guarantee these conditions. For example, a discontinuous / disconnected Pareto surface could generate dominated solutions, and a biased problem could result in non-uniform distributions. Therefore, we recommend callers filter solutions through a NondominatedPopulation, in particular one that maintains a spread of solutions.

      Furthermore, some implementations may not provide the corresponding decision variables for the solution. These implementations should indicate this by returning a solution with 0 decision variables.

      Specified by:
      generate in interface AnalyticalProblem
      Returns:
      a randomly-generated Pareto optimal solution to this problem