Package org.moeaframework.algorithm

Class EpsilonMOEA

java.lang.Object
org.moeaframework.algorithm.AbstractAlgorithm
org.moeaframework.algorithm.AbstractEvolutionaryAlgorithm
org.moeaframework.algorithm.EpsilonMOEA
All Implemented Interfaces:
Extensible, Algorithm, Configurable, EpsilonBoxEvolutionaryAlgorithm, EvolutionaryAlgorithm, Stateful
public class EpsilonMOEA extends AbstractEvolutionaryAlgorithm implements EpsilonBoxEvolutionaryAlgorithm
Implementation of the ε-MOEA algorithm. The ε-MOEA is a steady-state algorithm, meaning only one individual in the population is evolved per step, and uses an ε-dominance archive to maintain a well-spread set of Pareto-optimal solutions.

References:

  1. Deb et al. "A Fast Multi-Objective Evolutionary Algorithm for Finding Well-Spread Pareto-Optimal Solutions." KanGAL Report No 2003002. Feb 2003.

  • Constructor Details

    • EpsilonMOEA

      public EpsilonMOEA(Problem problem)
      Constructs the ε-MOEA algorithm with default settings.
      Parameters:
      problem - the problem

    • EpsilonMOEA

      public EpsilonMOEA(Problem problem, int initialPopulationSize, Population population, EpsilonBoxDominanceArchive archive, Selection selection, Variation variation, Initialization initialization)
      Constructs the ε-MOEA algorithm with the specified components.
      Parameters:
      problem - the problem being solved
      initialPopulationSize - the initial population size
      population - the population used to store solutions
      archive - the archive used to store the result
      selection - the selection operator
      variation - the variation operator
      initialization - the initialization method

    • EpsilonMOEA

      public EpsilonMOEA(Problem problem, int initialPopulationSize, Population population, EpsilonBoxDominanceArchive archive, Selection selection, Variation variation, Initialization initialization, DominanceComparator dominanceComparator)
      Constructs the ε-MOEA algorithm with the specified components.
      Parameters:
      problem - the problem being solved
      initialPopulationSize - the initial population size
      population - the population used to store solutions
      archive - the archive used to store the result
      selection - the selection operator
      variation - the variation operator
      initialization - the initialization method
      dominanceComparator - the dominance comparator used by the addToPopulation(org.moeaframework.core.Solution) method

  • Method Details

    • 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

    • addToPopulation

      protected void addToPopulation(Solution newSolution)
      Adds the new solution to the population if is non-dominated with the current population, removing either a randomly-selected dominated solution or a non-dominated solution.
      Parameters:
      newSolution - the new solution being added to the population

    • getArchive

      public EpsilonBoxDominanceArchive getArchive()
      Description copied from interface: EvolutionaryAlgorithm
      Returns the current non-dominated archive of the best solutions generated by this evolutionary algorithm, or null if no archive is used.
      Specified by:
      getArchive in interface EpsilonBoxEvolutionaryAlgorithm
      Specified by:
      getArchive in interface EvolutionaryAlgorithm
      Overrides:
      getArchive in class AbstractEvolutionaryAlgorithm
      Returns:
      the current non-dominated archive of the best solutions generated by this evolutionary algorithm, or null if no archive is used

    • setArchive

      public void setArchive(EpsilonBoxDominanceArchive archive)
      Sets the archive used by this algorithm. This value can not be set after initialization.
      Parameters:
      archive - the archive

    • 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).
      Specified by:
      applyConfiguration in interface Configurable
      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.
      Specified by:
      getConfiguration in interface Configurable
      Returns:
      the properties defining this instance