Package org.moeaframework.problem.WFG
Class WFG4
java.lang.Object
org.moeaframework.problem.AbstractProblem
org.moeaframework.problem.WFG.WFG
org.moeaframework.problem.WFG.WFG4
- All Implemented Interfaces:
AutoCloseable
,Problem
,AnalyticalProblem
The WFG4 test problem.
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Field Summary
Fields inherited from class org.moeaframework.problem.AbstractProblem
numberOfConstraints, numberOfObjectives, numberOfVariables
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Constructor Summary
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Method Summary
Methods inherited from class org.moeaframework.problem.WFG.WFG
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, getNumberOfConstraints, getNumberOfObjectives, getNumberOfVariables, isType
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Constructor Details
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WFG4
public WFG4(int M) Constructs a WFG4 problem instance with the specified number of objectives.- Parameters:
M
- the number of objectives for this problem
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WFG4
public WFG4(int k, int l, int M) Constructs a WFG4 problem instance with the specified number of position-related and distance-related variables and the specified number of objectives.- Parameters:
k
- the number of position-related variables for this probleml
- the number of distance-related variables for this problemM
- the number of objectives for this problem
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Method Details
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evaluate
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.- Parameters:
solution
- the solution to be evaluated
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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.- Returns:
- a randomly-generated Pareto optimal solution to this problem
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