Package org.moeaframework.algorithm

Class SMSEMOA

java.lang.Object

org.moeaframework.algorithm.AbstractAlgorithm

org.moeaframework.algorithm.AbstractEvolutionaryAlgorithm

org.moeaframework.algorithm.SMSEMOA

All Implemented Interfaces:

Extensible, Algorithm, Configurable, EvolutionaryAlgorithm, Stateful

public class SMSEMOA
extends AbstractEvolutionaryAlgorithm

Implementation of the S-metric Selection MOEA (SMS-MOEA). The S metric is also known as the hypervolume indicator.

References:

1. Emmerich, M., N. Beume, and B. Naujoks (2007). An EMO Algorithm Using the Hypervolume Measure as Selection Criterion. European Journal of Operational Research, 3:1653-1669.

Field Summary

Fields inherited from class org.moeaframework.algorithm.AbstractAlgorithm

initialized, numberOfEvaluations, problem, terminated

Constructor Summary

Constructors

Constructor	Description
SMSEMOA(Problem problem)	Constructs a new SMS-EMOA instance with default settings.
SMSEMOA(Problem problem, int initialPopulationSize, Initialization initialization, Variation variation, FitnessEvaluator fitnessEvaluator)	Constructs a new SMS-EMOA instance.

Method Summary

Modifier and Type	Method	Description
void	applyConfiguration(TypedProperties properties)	Applies the properties to this instance.
TypedProperties	getConfiguration()	Gets the current configuration of this instance.
FitnessEvaluator	getFitnessEvaluator()	Returns the fitness evaluator.
void	initialize()	Performs any initialization that is required by this algorithm.
protected void	iterate()	Performs one iteration of the algorithm.
void	setFitnessEvaluator(FitnessEvaluator fitnessEvaluator)	Sets the fitness evaluator.
void	setInitialPopulationSize(int initialPopulationSize)	Sets the initial population size.
void	setVariation(Variation variation)	Replaces the variation operator to be used by this algorithm.

Methods inherited from class org.moeaframework.algorithm.AbstractEvolutionaryAlgorithm

getArchive, getInitialization, getInitialPopulationSize, getPopulation, getResult, getVariation, loadState, saveState, setArchive, setInitialization, setPopulation

Methods inherited from class org.moeaframework.algorithm.AbstractAlgorithm

assertNotInitialized, evaluate, getExtensions, getNumberOfEvaluations, getProblem, isInitialized, isTerminated, step, terminate

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.Algorithm

evaluate, evaluateAll, evaluateAll, getNumberOfEvaluations, getProblem, isInitialized, isTerminated, run, run, step, terminate

Methods inherited from interface org.moeaframework.algorithm.extension.Extensible

addExtension, getExtensions, removeExtension

Constructor Details

SMSEMOA

public SMSEMOA(Problem problem)

Constructs a new SMS-EMOA instance with default settings.

Parameters:

problem - the problem

SMSEMOA

public SMSEMOA(Problem problem,
 int initialPopulationSize,
 Initialization initialization,
 Variation variation,
 FitnessEvaluator fitnessEvaluator)

Constructs a new SMS-EMOA instance.

Parameters:

problem - the problem

initialPopulationSize - the initial population size

initialization - the initialization operator

variation - the variation operator

fitnessEvaluator - the fitness evaluator

Method Details

setVariation

public void setVariation(Variation variation)

Description copied from class: AbstractEvolutionaryAlgorithm

Replaces the variation operator to be used by this algorithm.

Overrides:

setVariation in class AbstractEvolutionaryAlgorithm

Parameters:

variation - the variation operator

setInitialPopulationSize

public void setInitialPopulationSize(int initialPopulationSize)

Description copied from class: AbstractEvolutionaryAlgorithm

Sets the initial population size. This value can not be set after initialization.

Overrides:

setInitialPopulationSize in class AbstractEvolutionaryAlgorithm

Parameters:

initialPopulationSize - the initial population size

getFitnessEvaluator

public FitnessEvaluator getFitnessEvaluator()

Returns the fitness evaluator.

Returns:

the fitness evaluator

setFitnessEvaluator

public void setFitnessEvaluator(FitnessEvaluator fitnessEvaluator)

Sets the fitness evaluator. If null, will default to non-dominated sorting for selection.

Parameters:

fitnessEvaluator - the fitness evaluator

initialize

public void initialize()

Description copied from interface: Algorithm

Performs any initialization that is required by this algorithm. This method should only be called once, though the specific implementation may choose to no-op or throw AlgorithmInitializationException if called multiple times.

Implementations should always call super.initialize() to ensure the algorithm is initialized correctly.

Specified by:

initialize in interface Algorithm

Overrides:

initialize in class AbstractEvolutionaryAlgorithm

iterate

protected 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

applyConfiguration

public void applyConfiguration(TypedProperties properties)

Description copied from interface: Configurable

Applies the properties to this instance. It is strongly recommended to apply a configuration immediately after creating the instance, as some properties can not be changed after the class is used. Exceptions may be thrown if attempting to set such properties. After calling this method, we encourage users to call TypedProperties.warnIfUnaccessedProperties() to verify all properties were processed. This can identify simple mistakes like typos. If overriding this method, properties should only be updated if a new value is provided. Additionally, if updating any Configurable objects inside this object, they should be updated before calling super.applyConfiguration(properties).

Parameters:

properties - the user-defined properties

getConfiguration

public TypedProperties getConfiguration()

Description copied from interface: Configurable

Gets the current configuration of this instance. In theory, these properties should be able to create a duplicate instance. Note however, they are unlikely to behave identically due to random numbers and other transient fields.

Returns:

the properties defining this instance