Package org.moeaframework.problem.misc

Class Fonseca

java.lang.Object

org.moeaframework.problem.AbstractProblem

org.moeaframework.problem.misc.Fonseca

All Implemented Interfaces:

AutoCloseable, Problem, AnalyticalProblem

public class Fonseca
extends AbstractProblem
implements AnalyticalProblem

The Fonseca problem.

Properties:

• Connected Pareto set
• Concave Pareto front

References:

1. Fonseca, C. M. and Fleming, P. J. (1995). "An Overview of Evolutioary Algorithms in Multiobjective Optimization." Evolutionary Computation, vol. 3, no. 1, pp. 1-16.
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

Constructors

Constructor	Description
Fonseca()	Constructs the Fonseca problem.

Method Summary

Modifier and Type	Method	Description
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, getName, getNumberOfConstraints, getNumberOfObjectives, getNumberOfVariables

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.moeaframework.core.Problem

close, getName, getNumberOfConstraints, getNumberOfObjectives, getNumberOfVariables, isType

Constructor Details

Fonseca

public Fonseca()

Constructs the Fonseca 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