org.moeaframework.problem.misc
Class Fonseca2

java.lang.Object
  org.moeaframework.problem.AbstractProblem
      org.moeaframework.problem.misc.Fonseca2

All Implemented Interfaces:

Problem, AnalyticalProblem

public class Fonseca2
extends AbstractProblem
implements AnalyticalProblem

The Fonseca (2) problem.

Properties:

• Connected Pareto set
• Concave Pareto front

References:

1. Fonseca C. M. and Fleming, P. J. (1995). "Multiobjective Genetic Algorithms Made Easy: Selection, Sharing and Mating Restriction." Genetic Algorithms and Engineering Systems: Innovations and Applications, 12-14 Sept. 1995, pp. 45-52.
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

Fonseca2()
Constructs the Fonseca (2) problem with 3 decision variables.

Fonseca2(int numberOfVariables)
Constructs the Fonseca (2) problem with the specified number of decision variables.

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

Fonseca2

public Fonseca2()

Constructs the Fonseca (2) problem with 3 decision variables.

Fonseca2

public Fonseca2(int numberOfVariables)

Constructs the Fonseca (2) problem with the specified number of decision variables.

Parameters:

numberOfVariables - the number of decision variables

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