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Hybrid approaches to optimization and machine learning methods: a systematic literature review

2024Journal article Open access
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dc.contributor.authorAzevedo, Beatriz Flamia
dc.contributor.authorRocha, Ana Maria A.C.
dc.contributor.authorPereira, Ana I.
dc.date.accessioned2024-06-17T13:50:35Z
dc.date.available2024-06-17T13:50:35Z
dc.date.issued2024
dc.description.abstractNotably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.pt_PT
dc.description.sponsorshipOpen access funding provided by FCT|FCCN (b-on). This work has been supported by FCT— Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021 The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/ MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAzevedo, Beatriz Flamia; Rocha, Ana Maria A.C.; Pereira, Ana I. (2024). Hybrid approaches to optimization and machine learning methods: a systematic literature review. Machine Learning. ISSN 0885-6125. 113:7, p. 4055-4097pt_PT
dc.identifier.doi10.1007/s10994-023-06467-xpt_PT
dc.identifier.issn0885-6125
dc.identifier.urihttp://hdl.handle.net/10198/29907
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationLA/P/0007/2021pt_PT
dc.relationALGORITMI Research Center
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectClassificationpt_PT
dc.subjectClusteringpt_PT
dc.subjectHybrid methodspt_PT
dc.subjectLiterature reviewpt_PT
dc.subjectMachine learningpt_PT
dc.subjectOptimizationpt_PT
dc.titleHybrid approaches to optimization and machine learning methods: a systematic literature reviewpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleALGORITMI Research Center
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00319%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.citation.endPage4097pt_PT
oaire.citation.issue7pt_PT
oaire.citation.startPage4055pt_PT
oaire.citation.titleMachine Learningpt_PT
oaire.citation.volume113pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameAzevedo
person.familyNamePereira
person.givenNameBeatriz Flamia
person.givenNameAna I.
person.identifier.ciencia-id181E-855C-E62C
person.identifier.ciencia-id0716-B7C2-93E4
person.identifier.orcid0000-0002-8527-7409
person.identifier.orcid0000-0003-3803-2043
person.identifier.ridF-3168-2010
person.identifier.scopus-author-id15071961600
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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