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MONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Inteligencia Artificial Aplicada (GIAA)es
dc.contributor.authorMartí, Luis
dc.contributor.authorGarcía Herrero, Jesús
dc.contributor.authorBerlanga de Jesús, Antonio
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2021-11-04T10:39:51Z
dc.date.available2021-11-04T10:39:51Z
dc.date.issued2016-12-01
dc.description.abstractThe Extension Of Estimation Of Distribution Algorithms (Edas) To The Multiobjective Domain Has Led To Multi-Objective Optimization Edas (Moedas). Most Moedas Have Limited Themselves To Porting Single-Objective Edas To The Multi-Objective Domain. Although Moedas Have Proved To Be A Valid Approach, The Last Point Is An Obstacle To The Achievement Of A Significant Improvement Regarding "Standard" Multi-Objective Optimization Evolutionary Algorithms. Adapting The Model-Building Algorithm Is One Way To Achieve A Substantial Advance. Most Model-Building Schemes Used So Far By Edas Employ Off-The-Shelf Machine Learning Methods. However, The Model-Building Problem Has Particular Requirements That Those Methods Do Not Meet And Even Evade. The Focus Of This Paper Is On The Model- Building Issue And How It Has Not Been Properly Understood And Addressed By Most Moedas. We Delve Down Into The Roots Of This Matter And Hypothesize About Its Causes. To Gain A Deeper Understanding Of The Subject We Propose A Novel Algorithm Intended To Overcome The Draw-Backs Of Current Moedas. This New Algorithm Is The Multi-Objective Neural Estimation Of Distribution Algorithm (Moneda). Moneda Uses A Modified Growing Neural Gas Network For Model-Building (Mb-Gng). Mb-Gng Is A Custom-Made Clustering Algorithm That Meets The Above Demands. Thanks To Its Custom-Made Model-Building Algorithm, The Preservation Of Elite Individuals And Its Individual Replacement Scheme, Moneda Is Capable Of Scalably Solving Continuous Multi-Objective Optimization Problems. It Performs Better Than Similar Algorithms In Terms Of A Set Of Quality Indicators And Computational Resource Requirements.en
dc.description.sponsorshipThis work has been funded in part by projects CNPq BJT 407851/2012-7, FAPERJ APQ1 211.451/2015, MINECO TEC2014-57022-C2-2-R and TEC2012-37832-C02-01.en
dc.identifier.bibliographicCitationMartí, L., García, J., Berlanga, A. et al. MONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm. J Glob Optim 66, 729–768 (2016). https://doi.org/10.1007/s10898-016-0415-7en
dc.identifier.doihttps://doi.org/10.1007/s10898-016-0415-7
dc.identifier.issn1573-2916
dc.identifier.publicationfirstpage729
dc.identifier.publicationissue4
dc.identifier.publicationlastpage768
dc.identifier.publicationtitleJournal of Global Optimizationen
dc.identifier.publicationvolume66
dc.identifier.urihttps://hdl.handle.net/10016/33535
dc.identifier.uxxiAR/0000019315
dc.language.isoengen
dc.publisherSpringeren
dc.relation.projectIDGobierno de España. TEC2012-37832-C02-01es
dc.relation.projectIDGobierno de España. TEC2014-57022-C2-2-Res
dc.rights© 2016, Springer Science Business Media New Yorken
dc.rights.accessRightsopen accessen
dc.subject.ecienciaInformáticaes
dc.subject.othermulti-objective optimization problemsen
dc.subject.otherestimation of distribution algorithmsen
dc.subject.othermodel-building problemsen
dc.subject.otherneural networksen
dc.subject.othergrowing neural gasen
dc.titleMONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithmen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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