Package org.moeaframework.algorithm

Class MSOPS

java.lang.Object

org.moeaframework.algorithm.AbstractAlgorithm

org.moeaframework.algorithm.AbstractEvolutionaryAlgorithm

org.moeaframework.algorithm.MSOPS

All Implemented Interfaces:

Algorithm, Configurable, EvolutionaryAlgorithm, Stateful

public class MSOPS
extends AbstractEvolutionaryAlgorithm

Implementation of the Multiple Single Objective Pareto Sampling (MSOPS) algorithm. This implementation only supports differential evolution.

References:

1. E. J. Hughes. "Multiple Single Objective Pareto Sampling." 2003 Congress on Evolutionary Computation, pp. 2678-2684.
2. Matlab source code available from http://code.evanhughes.org/.

See Also:

• MSOPSRankedPopulation

Field Summary

Fields inherited from class org.moeaframework.algorithm.AbstractAlgorithm

initialized, numberOfEvaluations, problem, terminated

Constructor Summary

Constructors

Constructor	Description
MSOPS(Problem problem)	Constructs a new MSOPS instance with default settings.
MSOPS(Problem problem, int initialPopulationSize, MSOPSRankedPopulation population, DifferentialEvolutionSelection selection, DifferentialEvolutionVariation variation, Initialization initialization)	Constructs a new instance of the MSOPS algorithm.

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.
MSOPSRankedPopulation	getPopulation()	Returns the current population of this evolutionary algorithm.
DifferentialEvolutionVariation	getVariation()	Returns the variation operator currently in use by this algorithm.
protected void	iterate()	Performs one iteration of the algorithm.
void	setInitialPopulationSize(int initialPopulationSize)	Sets the initial population size.
void	setVariation(DifferentialEvolutionVariation variation)	Replaces the differential evolution variation operator to be used by this algorithm.

Methods inherited from class org.moeaframework.algorithm.AbstractEvolutionaryAlgorithm

getArchive, getInitialization, getInitialPopulationSize, getResult, initialize, loadState, saveState, setArchive, setInitialization, setPopulation, setVariation

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

MSOPS

public MSOPS(Problem problem)

Constructs a new MSOPS instance with default settings.

Parameters:

problem - the problem being solved

MSOPS

public MSOPS(Problem problem,
 int initialPopulationSize,
 MSOPSRankedPopulation population,
 DifferentialEvolutionSelection selection,
 DifferentialEvolutionVariation variation,
 Initialization initialization)

Constructs a new instance of the MSOPS algorithm.

Parameters:

problem - the problem being solved

initialPopulationSize - the initial population size

population - the population supporting MSOPS ranking

selection - the differential evolution selection operator

variation - the differential evolution variation operator

initialization - the initialization method

Method Details

getVariation

public DifferentialEvolutionVariation getVariation()

Description copied from class: AbstractEvolutionaryAlgorithm

Returns the variation operator currently in use by this algorithm.

Overrides:

getVariation in class AbstractEvolutionaryAlgorithm

Returns:

the variation operator

setVariation

public void setVariation(DifferentialEvolutionVariation variation)

Replaces the differential evolution variation operator to be used by this algorithm.

Parameters:

variation - the differential evolution 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

getPopulation

public MSOPSRankedPopulation getPopulation()

Description copied from interface: EvolutionaryAlgorithm

Returns the current population of this evolutionary algorithm.

Specified by:

getPopulation in interface EvolutionaryAlgorithm

Overrides:

getPopulation in class AbstractEvolutionaryAlgorithm

Returns:

the current population of this evolutionary algorithm

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