
MOEA Framework 2.12 API Specification 

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java.lang.Object org.moeaframework.algorithm.AbstractAlgorithm org.moeaframework.algorithm.MOEAD
public class MOEAD
Implementation of MOEA/D, the multiobjective evolutionary algorithm with decomposition. This implementation supports both the original MOEA/D specification from [1] as well as the utilitybased search extension from [2].
References:
Field Summary 

Fields inherited from class org.moeaframework.algorithm.AbstractAlgorithm 

initialized, numberOfEvaluations, problem, terminated 
Constructor Summary  

MOEAD(Problem problem,
int neighborhoodSize,
Initialization initialization,
Variation variation,
double delta,
double eta)
Constructs the MOEA/D algorithm with the specified components. 

MOEAD(Problem problem,
int neighborhoodSize,
Initialization initialization,
Variation variation,
double delta,
double eta,
int updateUtility)
Constructs the MOEA/D algorithm with the specified components. 

MOEAD(Problem problem,
int neighborhoodSize,
WeightGenerator weightGenerator,
Initialization initialization,
Variation variation,
double delta,
double eta)
Constructs the MOEA/D algorithm with the specified components. 

MOEAD(Problem problem,
int neighborhoodSize,
WeightGenerator weightGenerator,
Initialization initialization,
Variation variation,
double delta,
double eta,
int updateUtility)
Constructs the MOEA/D algorithm with the specified components. 
Method Summary  

NondominatedPopulation 
getResult()
Returns the current bestknown result. 
Serializable 
getState()
Returns a Serializable object representing the internal state of
this algorithm. 
void 
initialize()
Performs any initialization that is required by this algorithm. 
void 
iterate()
Performs one iteration of the algorithm. 
void 
setState(Object objState)
Sets the internal state of of this algorithm. 
protected void 
updateUtility()
Updates the utility of each individual. 
Methods inherited from class org.moeaframework.algorithm.AbstractAlgorithm 

evaluate, evaluateAll, evaluateAll, finalize, getNumberOfEvaluations, getProblem, isInitialized, isTerminated, step, terminate 
Methods inherited from class java.lang.Object 

clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait 
Constructor Detail 

public MOEAD(Problem problem, int neighborhoodSize, Initialization initialization, Variation variation, double delta, double eta, int updateUtility)
problem
 the problem being solvedneighborhoodSize
 the size of the neighborhood used for mating,
which must be at least variation.getArity()1
.initialization
 the initialization methodvariation
 the variation operatordelta
 the probability of mating with a solution in the
neighborhood rather than the entire populationeta
 the maximum number of population slots a solution can replaceupdateUtility
 the frequency, in generations, in which utility
values are updated; set to 50
to use the recommended
update frequency or 1
to disable utilitybased search.public MOEAD(Problem problem, int neighborhoodSize, Initialization initialization, Variation variation, double delta, double eta)
problem
 the problem being solvedneighborhoodSize
 the size of the neighborhood used for mating,
which must be at least variation.getArity()1
.initialization
 the initialization methodvariation
 the variation operatordelta
 the probability of mating with a solution in the
neighborhood rather than the entire populationeta
 the maximum number of population slots a solution can replacepublic MOEAD(Problem problem, int neighborhoodSize, WeightGenerator weightGenerator, Initialization initialization, Variation variation, double delta, double eta, int updateUtility)
problem
 the problem being solvedneighborhoodSize
 the size of the neighborhood used for mating,
which must be at least variation.getArity()1
.weightGenerator
 the weight generatorinitialization
 the initialization method, which must generate the
same number of solutions as weightsvariation
 the variation operatordelta
 the probability of mating with a solution in the
neighborhood rather than the entire populationeta
 the maximum number of population slots a solution can replaceupdateUtility
 the frequency, in generations, in which utility
values are updated; set to 50
to use the recommended
update frequency or 1
to disable utilitybased search.public MOEAD(Problem problem, int neighborhoodSize, WeightGenerator weightGenerator, Initialization initialization, Variation variation, double delta, double eta)
problem
 the problem being solvedneighborhoodSize
 the size of the neighborhood used for mating,
which must be at least variation.getArity()1
.weightGenerator
 the weight generatorinitialization
 the initialization method, which must generate the
same number of solutions as weightsvariation
 the variation operatordelta
 the probability of mating with a solution in the
neighborhood rather than the entire populationeta
 the maximum number of population slots a solution can replaceMethod Detail 

public void initialize()
AbstractAlgorithm
AbstractAlgorithm.step()
, but may also be called manually prior to any invocations
of step
. Implementations should always invoke
super.initialize()
to ensure the hierarchy is initialized
correctly.
initialize
in class AbstractAlgorithm
public NondominatedPopulation getResult()
Algorithm
protected void updateUtility()
public void iterate()
AbstractAlgorithm
iterate
in class AbstractAlgorithm
public Serializable getState() throws NotSerializableException
Algorithm
Serializable
object representing the internal state of
this algorithm.
getState
in interface Algorithm
getState
in class AbstractAlgorithm
Serializable
object representing the internal state of
this algorithm
NotSerializableException
 if this algorithm does not support
serializationpublic void setState(Object objState) throws NotSerializableException
Algorithm
setState
in interface Algorithm
setState
in class AbstractAlgorithm
objState
 the internal state of this algorithm
NotSerializableException
 if this algorithm does not support
serialization

MOEA Framework 2.12 API Specification 

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