Package org.moeaframework.problem.MaF

Class MaF14

java.lang.Object

org.moeaframework.problem.LSMOP.LSMOP

org.moeaframework.problem.MaF.MaF14

All Implemented Interfaces:

AutoCloseable, Problem, AnalyticalProblem

public class MaF14
extends LSMOP
implements AnalyticalProblem

The MaF14 test problem, which is identical to the LSMOP3 test problem with N_k=2 and N_ns=20. This problem exhibits the following properties:

• Linear Pareto front
• Partially separable decision variables
• Non-uniform correlations between decision variables and objectives

Field Summary

Fields inherited from class org.moeaframework.problem.LSMOP.LSMOP

DEFAULT_N_k, DEFAULT_N_ns

Constructor Summary

Constructors

Constructor	Description
MaF14(int numberOfObjectives)	Constructs an MaF14 test problem with the specified number of objectives.

Method Summary

Modifier and Type	Method	Description
Solution	generate()	Returns a randomly-generated solution using the analytical solution to this problem.

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

close, evaluate, getName, getNumberOfConstraints, getNumberOfObjectives, getNumberOfVariables, newSolution

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, evaluate, getName, getNumberOfConstraints, getNumberOfObjectives, getNumberOfVariables, isType, newSolution

Constructor Details

MaF14

public MaF14(int numberOfObjectives)

Constructs an MaF14 test problem with the specified number of objectives.

Parameters:

numberOfObjectives - the number of objectives for this problem

Method Details

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