Package org.moeaframework.problem.MaF

Class MaF1

java.lang.Object

org.moeaframework.problem.AbstractProblem

org.moeaframework.problem.DTLZ.DTLZ

org.moeaframework.problem.MaF.MaF1

All Implemented Interfaces:

AutoCloseable, Problem, AnalyticalProblem

public class MaF1
extends DTLZ
implements AnalyticalProblem

The MaF1 test problem, which is a modified inverted version of the DTLZ1 test problem. This problem exhibits the following properties:

• Linear Pareto front
• No single optimal solution in any subset of the objectives

Field Summary

Fields inherited from class org.moeaframework.problem.AbstractProblem

numberOfConstraints, numberOfObjectives, numberOfVariables

Constructor Summary

Constructors

Constructor	Description
MaF1(int numberOfObjectives)	Constructs an MaF1 test problem with the specified number of objectives.

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.

Methods inherited from class org.moeaframework.problem.DTLZ.DTLZ

g1, g2, generateAt, getName, newSolution

Methods inherited from class org.moeaframework.problem.AbstractProblem

close, 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, newSolution

Constructor Details

MaF1

public MaF1(int numberOfObjectives)

Constructs an MaF1 test problem with the specified number of objectives.

Parameters:

numberOfObjectives - the number of objectives for this 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

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