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Salp Swarm Optimization

2022-03-01Artigo científico Acesso aberto
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dc.contributor.authorCastelli, Mauro
dc.contributor.authorManzoni, Luca
dc.contributor.authorMariot, Luca
dc.contributor.authorNobile, Marco S.
dc.contributor.authorTangherloni, Andrea
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblElsevier Science B.V., Amsterdam.
dc.date.accessioned2021-10-22T03:43:45Z
dc.date.available2024-12-31T01:31:25Z
dc.date.embargoedUntil2023-10-16
dc.date.issued2022-03-01
dc.descriptionCastelli, M., Manzoni, L., Mariot, L., Nobile, M. S., & Tangherloni, A. (2022). Salp Swarm Optimization: A critical review. Expert Systems with Applications, 189, 1-12. [116029]. [Advanced online publication on 16 October 2021]. Doi: https://doi.org/10.1016/j.eswa.2021.116029.---%ABS1% ---Funding Information: This work was supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia), Portugal by the projects GADgET ( DSAIPA/DS/0022/2018 ) and the financial support from the Slovenian Research Agency, Republic of Slovenia (research core funding no. P5-0410 ).
dc.description.abstractIn the crowded environment of bio-inspired population-based metaheuristics, the Salp Swarm Optimization (SSO) algorithm recently appeared and immediately gained a lot of momentum. Inspired by the peculiar spatial arrangement of salp colonies, which are displaced in long chains following a leader, this algorithm seems to provide an interesting optimization performance. However, the original work was characterized by some conceptual and mathematical flaws, which influenced all ensuing papers on the subject. In this manuscript, we perform a critical review of SSO, highlighting all the issues present in the literature and their negative effects on the optimization process carried out by this algorithm. We also propose a mathematically correct version of SSO, named Amended Salp Swarm Optimizer (ASSO) that fixes all the discussed problems. We benchmarked the performance of ASSO on a set of tailored experiments, showing that it is able to achieve better results than the original SSO. Finally, we performed an extensive study aimed at understanding whether SSO and its variants provide advantages compared to other metaheuristics. The experimental results, where SSO cannot outperform simple well-known metaheuristics, suggest that the scientific community can safely abandon SSO.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent12
dc.format.extent1761780
dc.identifier.doi10.1016/j.eswa.2021.116029
dc.identifier.issn0957-4174
dc.identifier.otherPURE: 34401364
dc.identifier.otherPURE UUID: 982b0eeb-ffd7-4e5f-ab09-fa94a646129e
dc.identifier.othercrossref: 10.1016/j.eswa.2021.116029
dc.identifier.otherScopus: 85117771331
dc.identifier.otherWOS: 000717676900012
dc.identifier.otherORCID: /0000-0002-8793-1451/work/101864482
dc.identifier.urihttp://hdl.handle.net/10362/126504
dc.identifier.urlhttps://gitlab.com/andrea-tango/asso
dc.identifier.urlhttps://www.scopus.com/pages/publications/85117771331
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:000717676900012
dc.identifier.urlhttps://linkinghub.elsevier.com/retrieve/pii/S0957417421013750
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0022%2F2018/PT
dc.subjectMetaheuristics
dc.subjectGlobal optimization
dc.subjectBound constrained optimization
dc.subjectShift invariant functions
dc.subjectGeneral Engineering
dc.subjectComputer Science Applications
dc.subjectArtificial Intelligence
dc.titleSalp Swarm Optimizationen
dc.title.subtitleA critical reviewen
dc.typejournal article
degois.publication.firstPage1
degois.publication.lastPage12
degois.publication.titleExpert Systems with Applications
degois.publication.volume189
dspace.entity.typePublication
oaire.awardNumberDSAIPA/DS/0022/2018
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0022%2F2018/PT
oaire.fundingStream3599-PPCDT
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublicationc35c919f-29eb-4019-b809-622c143b6c56
relation.isProjectOfPublication.latestForDiscoveryc35c919f-29eb-4019-b809-622c143b6c56

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