Package org.moeaframework.problem.misc

Class Murata

java.lang.Object
org.moeaframework.problem.AbstractProblem
org.moeaframework.problem.misc.Murata
All Implemented Interfaces:
AutoCloseable, Named, AnalyticalProblem, Problem
public class Murata extends AbstractProblem implements AnalyticalProblem
The Murata problem. The optimum is defined by (x, 2) where 1 <= x < = 4.

Properties:

  • Connected Pareto set
  • Concave Pareto front

References:

  1. Murata, T. and Ishibuchi, H. (1995). "MOGA: Multi-Objective Genetic Algorithms." IEEE International Conference on Evolutionary Computation, pp. 289-294.
  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

    • Murata

      public Murata()
      Constructs the Murata 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. 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