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Towards a high-performance implementation of the MCSFilter optimization algorithm

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dc.contributor.authorAraújo, Leonardo Oliveira
dc.contributor.authorPacheco, Maria F.
dc.contributor.authorRufino, José
dc.contributor.authorFernandes, Florbela P.
dc.date.accessioned2022-04-05T14:19:07Z
dc.date.available2022-04-05T14:19:07Z
dc.date.issued2021
dc.description.abstractMultistart Coordinate Search Filter (MCSFilter) is an optimization method suitable to find all minimizers – both local and global – of a non convex problem, with simple bounds or more generic constraints. Like many other optimization algorithms, it may be used in industrial contexts, where execution time may be critical in order to keep a production process within safe and expected bounds. MCSFilter was first implemented in MATLAB and later in Java (which introduced a significant performance gain). In this work, a comparison is made between these two implementations and a novel one in C that aims at further performance improvements. For the comparison, the problems addressed are bound constraint, with small dimension (between 2 and 10) and multiple local and global solutions. It is possible to conclude that the average time execution for each problem is considerable smaller when using the Java and C implementations, and that the current C implementation, though not yet fully optimized, already exhibits a significant speedup.pt_PT
dc.description.sponsorshipThis work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAraújo, Leonardo; Pacheco, Maria F.; Rufino, José; Fernandes, Florbela P. (2021). Towards a high-performance implementation of the MCSFilter optimization algorithm. In Pereira, Ana I.; Fernandes, Florbela P.; Coelho, João Paulo; Teixeira, João Paulo; Pacheco, Maria F.; Alves, Paulo; Lopes, Rui Pedro (Eds.) Optimization, learning algorithms and applications: first International Conference, OL2A 2021. Cham: Springer Nature. p. 15-30. ISBN 978-3-030-91884-2pt_PT
dc.identifier.doi10.1007/978-3-030-91885-9_2pt_PT
dc.identifier.isbn978-3-030-91884-2
dc.identifier.urihttp://hdl.handle.net/10198/25353
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectOptimizationpt_PT
dc.subjectMCSFilter methodpt_PT
dc.subjectMatLabpt_PT
dc.subjectCpt_PT
dc.subjectJavapt_PT
dc.subjectPerformancept_PT
dc.titleTowards a high-performance implementation of the MCSFilter optimization algorithmpt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardNumberUIDB/05757/2020
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.citation.conferencePlaceBragançapt_PT
oaire.citation.endPage30pt_PT
oaire.citation.startPage15pt_PT
oaire.citation.titleOptimization, learning algorithms and applications: first International Conference, OL2A 2021pt_PT
oaire.citation.volume1488pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNamePacheco
person.familyNameRufino
person.familyNameFernandes
person.givenNameMaria F.
person.givenNameJosé
person.givenNameFlorbela P.
person.identifier.ciencia-idF319-DAC3-8F15
person.identifier.ciencia-idC414-F47F-6323
person.identifier.ciencia-id501D-6FD0-CC53
person.identifier.orcid0000-0001-7915-0391
person.identifier.orcid0000-0002-1344-8264
person.identifier.orcid0000-0001-9542-4460
person.identifier.scopus-author-id36802474600
person.identifier.scopus-author-id55947199100
person.identifier.scopus-author-id35179471000
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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