Package org.moeaframework.algorithm

Class IBEA

java.lang.Object

org.moeaframework.algorithm.AbstractAlgorithm

org.moeaframework.algorithm.AbstractEvolutionaryAlgorithm

org.moeaframework.algorithm.IBEA

All Implemented Interfaces:

Algorithm, Configurable, EvolutionaryAlgorithm, Stateful

public class IBEA
extends AbstractEvolutionaryAlgorithm

Implementation of the Indicator-Based Evolutionary Algorithm (IBEA). Instead of using Pareto dominance to evaluate the quality of solutions, IBEA uses an indicator function (typically hypervolume but other indicator functions can be specified).

References:

1. Zitzler, E. and S. Kunzli (2004). Indicator-Based Selection in Multiobjective Search. Parallel Problem Solving from Nature (PPSN VIII), pp. 832-842.

Field Summary

Fields inherited from class org.moeaframework.algorithm.AbstractAlgorithm

initialized, numberOfEvaluations, problem, terminated

Constructor Summary

Constructors

Constructor	Description
IBEA(Problem problem)	Constructs a new IBEA instance with default settings.
IBEA(Problem problem, int initialPopulationSize, NondominatedPopulation archive, Initialization initialization, Variation variation, IndicatorFitnessEvaluator fitnessEvaluator)	Constructs a new IBEA 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.
IndicatorFitnessEvaluator	getFitnessEvaluator()	Returns the indicator-based fitness evaluator.
protected void	initialize()	Performs any initialization that is required by this algorithm.
protected void	iterate()	Performs one iteration of the algorithm.
void	setFitnessEvaluator(IndicatorFitnessEvaluator fitnessEvaluator)	Sets the indicator-based 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, evaluateAll, evaluateAll, 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, getNumberOfEvaluations, getProblem, isTerminated, run, run, step, terminate

Constructor Details

IBEA

public IBEA(Problem problem)

Constructs a new IBEA instance with default settings.

Parameters:

problem - the problem

IBEA

public IBEA(Problem problem,
 int initialPopulationSize,
 NondominatedPopulation archive,
 Initialization initialization,
 Variation variation,
 IndicatorFitnessEvaluator fitnessEvaluator)

Constructs a new IBEA instance.

Parameters:

problem - the problem

initialPopulationSize - the initial population size

archive - the external archive; or null if no external archive is used

initialization - the initialization operator

variation - the variation operator

fitnessEvaluator - the indicator fitness evaluator to use (e.g., hypervolume additive-epsilon indicator)

Method Details

getFitnessEvaluator

public IndicatorFitnessEvaluator getFitnessEvaluator()

Returns the indicator-based fitness evaluator.

Returns:

the indicator-based fitness evaluator

setFitnessEvaluator

public void setFitnessEvaluator(IndicatorFitnessEvaluator fitnessEvaluator)

Sets the indicator-based fitness evaluator. This value can not be null.

Parameters:

fitnessEvaluator - the indicator-based fitness evaluator

initialize

protected void initialize()

Description copied from class: AbstractAlgorithm

Performs any initialization that is required by this algorithm. This method is called automatically on the first invocation of AbstractAlgorithm.step(). Implementations should always invoke super.initialize() to ensure the algorithm is initialized correctly.

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

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

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