Package org.moeaframework.problem.misc

Class Laumanns

java.lang.Object
org.moeaframework.problem.AbstractProblem
org.moeaframework.problem.misc.Laumanns
All Implemented Interfaces:
AutoCloseable, Problem, AnalyticalProblem
public class Laumanns extends AbstractProblem implements AnalyticalProblem
The Laumanns problem. The optimum points like on the line (x, 0) with -2 <= x <= 0.

Properties:

  • Connected Pareto set
  • Disconnected Pareto front
  • Convex Pareto front

References:

  1. Laumanns, M., Rudolph, G., and Schwefel, H. (1998). "A Spatial Predator-Prey Approach to Multi-Objective Optimization: A Preliminary Study." Proceedings of the Parallel Problem Solving from Nature, Springer, pp. 241-249.
  2. Van Veldhuizen, D. A (1999). "Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations." Air Force Institute of Technology, Ph.D. Thesis, Appendix B.

  • Constructor Details

    • Laumanns

      public Laumanns()
      Constructs the Laumanns 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

    • newSolution

      public Solution newSolution()
      Description copied from interface: Problem
      Returns a new solution for this problem. Implementations must initialize the variables so that the valid range of values is defined, but typically leave the actual value at a default or undefined state.
      Specified by:
      newSolution in interface Problem
      Returns:
      a new solution for this problem

    • generate

      public Solution generate()
      Description copied from interface: AnalyticalProblem
      Returns a randomly-generated solution using the analytical solution to this problem. Note however that discontinuous Pareto surfaces may result in some solutions generated by this method being dominated by other generated solutions. It is therefore recommended using a NondominatedPopulation to remove dominated solutions prior to using the generated reference set.

      The generated solutions should be spread uniformly across the entire Pareto frontier; however, this is a suggestion and is not a requirement of this interface.

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