org.moeaframework.problem.misc
Class Binh4

java.lang.Object
  org.moeaframework.problem.AbstractProblem
      org.moeaframework.problem.misc.Binh4

All Implemented Interfaces:

Problem, AnalyticalProblem

public class Binh4
extends AbstractProblem
implements AnalyticalProblem

The Binh (4) problem. The global feasible optimum is at (3.0, 0.5).

Properties:

• Connected Pareto set
• Degenerate, concave Pareto front
• Constrained

References:

1. Binh, T. T., and Korn, U. (1997). "Multiobjective Evolution Strategy with Linear and Nonlinear Constraints." Proc. of the 15th IMACS World Congress on Scientific Computation, Modeling and Applied Mathematics, pp. 357-362.
2. Van Veldhuizen, D. A (1999). "Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations." Air Force Institute of Technology, Ph.D. Thesis, Appendix B.

Field Summary

Fields inherited from class org.moeaframework.problem.AbstractProblem

numberOfConstraints, numberOfObjectives, numberOfVariables

Constructor Summary

Binh4()
Constructs the Binh (4) problem.

Method Summary

void	evaluate(Solution solution) Evaluates the solution, updating the solution's objectives in place.
Solution	generate() Returns a randomly-generated solution using the analytical solution to this problem.
Solution	newSolution() Returns a new solution for this problem.

Methods inherited from class org.moeaframework.problem.AbstractProblem

close, finalize, getName, getNumberOfConstraints, getNumberOfObjectives, getNumberOfVariables

Methods inherited from class java.lang.Object

clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.moeaframework.core.Problem

close, getName, getNumberOfConstraints, getNumberOfObjectives, getNumberOfVariables

Constructor Detail

Binh4

public Binh4()

Constructs the Binh (4) problem.

Method Detail

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 may 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 removed 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