org.moeaframework.algorithm
Class MOEAD

java.lang.Object
  org.moeaframework.algorithm.AbstractAlgorithm
      org.moeaframework.algorithm.MOEAD

All Implemented Interfaces:

Algorithm

public class MOEAD
extends AbstractAlgorithm

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 utility-based search extension from [2].

References:

1. Li, H. and Zhang, Q. "Multiobjective Optimization problems with Complicated Pareto Sets, MOEA/D and NSGA-II." IEEE Transactions on Evolutionary Computation, 13(2):284-302, 2009.
2. Zhang, Q., et al. "The Performance of a New Version of MOEA/D on CEC09 Unconstrained MOP Test Instances." IEEE Congress on Evolutionary Computation, 2009.

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.

Method Summary

NondominatedPopulation	getResult() Returns the current best-known 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.

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

MOEAD

public MOEAD(Problem problem,
             int neighborhoodSize,
             Initialization initialization,
             Variation variation,
             double delta,
             double eta,
             int updateUtility)

Constructs the MOEA/D algorithm with the specified components. This version of MOEA/D uses utility-based search as described in [2].

Parameters:

problem - the problem being solved

neighborhoodSize - the size of the neighborhood used for mating, which must be at least variation.getArity()-1.

initialization - the initialization method

variation - the variation operator

delta - the probability of mating with a solution in the neighborhood rather than the entire population

eta - the maximum number of population slots a solution can replace

updateUtility - the frequency, in generations, in which utility values are updated; set to -1 to disable utility-based search

MOEAD

public MOEAD(Problem problem,
             int neighborhoodSize,
             Initialization initialization,
             Variation variation,
             double delta,
             double eta)

Constructs the MOEA/D algorithm with the specified components. This constructs the original MOEA/D implementation without utility-based search.

Parameters:

problem - the problem being solved

neighborhoodSize - the size of the neighborhood used for mating, which must be at least variation.getArity()-1.

initialization - the initialization method

variation - the variation operator

delta - the probability of mating with a solution in the neighborhood rather than the entire population

eta - the maximum number of population slots a solution can replace

Method Detail

initialize

public void initialize()

Description copied from class: AbstractAlgorithm

Performs any initialization that is required by this algorithm. This method is called automatically by the first invocation of 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.

Overrides:

initialize in class AbstractAlgorithm

getResult

public NondominatedPopulation getResult()

Description copied from interface: Algorithm

Returns the current best-known result.

Returns:

the current best-known result

iterate

public void iterate()

Description copied from class: AbstractAlgorithm

Performs one iteration of the algorithm. This method should be overridden by implementations to perform each logical iteration of the algorithm.

Specified by:

iterate in class AbstractAlgorithm

getState

public Serializable getState()
                      throws NotSerializableException

Description copied from interface: Algorithm

Returns a Serializable object representing the internal state of this algorithm.

Specified by:

getState in interface Algorithm

Overrides:

getState in class AbstractAlgorithm

Returns:

a Serializable object representing the internal state of this algorithm

Throws:

NotSerializableException - if this algorithm does not support serialization

setState

public void setState(Object objState)
              throws NotSerializableException

Description copied from interface: Algorithm

Sets the internal state of of this algorithm.

Specified by:

setState in interface Algorithm

Overrides:

setState in class AbstractAlgorithm

Parameters:

objState - the internal state of this algorithm

Throws:

NotSerializableException - if this algorithm does not support serialization