Package org.moeaframework.problem.DTLZ
Class DTLZ1
java.lang.Object
org.moeaframework.problem.AbstractProblem
org.moeaframework.problem.DTLZ.DTLZ
org.moeaframework.problem.DTLZ.DTLZ1
- All Implemented Interfaces:
AutoCloseable
,Problem
,AnalyticalProblem
The DTLZ1 test problem.
-
Field Summary
Fields inherited from class org.moeaframework.problem.AbstractProblem
numberOfConstraints, numberOfObjectives, numberOfVariables
-
Constructor Summary
-
Method Summary
Methods inherited from class org.moeaframework.problem.DTLZ.DTLZ
newSolution
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
-
DTLZ1
public DTLZ1(int numberOfObjectives) Constructs a DTLZ1 test problem with the specified number of objectives. This is equivalent to callingnew DTLZ1(numberOfObjectives+4, numberOfObjectives)
.- Parameters:
numberOfObjectives
- the number of objectives for this problem
-
DTLZ1
public DTLZ1(int numberOfVariables, int numberOfObjectives) Constructs a DTLZ1 test problem with the specified number of variables and objectives.- Parameters:
numberOfVariables
- the number of variables for this problemnumberOfObjectives
- the number of objectives for this problem
-
-
Method Details
-
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
-
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 aNondominatedPopulation
to remove 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.
- Returns:
- a randomly-generated Pareto optimal solution to this problem
-