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https://hdl.handle.net/1822/78199
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Campo DC | Valor | Idioma |
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dc.contributor.author | Hendrix, Eligius M. T. | por |
dc.contributor.author | Rocha, Ana Maria A. C. | por |
dc.date.accessioned | 2022-06-02T15:04:24Z | - |
dc.date.available | 2022-06-02T15:04:24Z | - |
dc.date.issued | 2021 | - |
dc.identifier.isbn | 978-3-030-86975-5 | por |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://hdl.handle.net/1822/78199 | - |
dc.description.abstract | In engineering optimization with continuous variables, the use of Stochastic Global Optimization (SGO) algorithms is popular due to the easy availability of codes. All algorithms have a global and local search character, where the global behaviour tries to avoid getting trapped in local optima and the local behaviour intends to reach the lowest objective function values. As the algorithm parameter set includes a final convergence criterion, the algorithm might be running for a while around a reached minimum point. Our question deals with the local search behaviour after the algorithm reached the final stage. How fast do practical SGO algorithms actually converge to the minimum point? To investigate this question, we run implementations of well known SGO algorithms in a final local phase stage. | por |
dc.description.sponsorship | - This paper has been supported by The Spanish Ministry (RTI2018-095993-B-I00) in part financed by the European Regional Development Fund (ERDF) and by FCT Fundacao para a Ciencia e Tecnologia within the Project Scope: UIDB/00319/2020. | por |
dc.language.iso | eng | por |
dc.publisher | Springer | por |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00319%2F2020/PT | por |
dc.rights | openAccess | por |
dc.subject | Stochastic global optimization | por |
dc.subject | Evolutionary algorithms | por |
dc.subject | Convergence | por |
dc.subject | Nonlinear optimization | por |
dc.title | On local convergence of stochastic global optimization algorithms | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-030-86976-2_31 | por |
oaire.citationStartPage | 456 | por |
oaire.citationEndPage | 472 | por |
oaire.citationVolume | 12953 | por |
dc.date.updated | 2022-06-01T18:41:46Z | - |
dc.identifier.doi | 10.1007/978-3-030-86976-2_31 | por |
dc.identifier.eisbn | 978-3-030-86976-2 | - |
dc.subject.wos | Science & Technology | - |
sdum.export.identifier | 11227 | - |
sdum.journal | Lecture Notes in Computer Science | por |
sdum.conferencePublication | International Conference on Computational Science and Its Applications | por |
sdum.bookTitle | Computational Science and Its Applications – ICCSA 2021 | por |
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Ficheiro | Descrição | Tamanho | Formato | |
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On_Local_Convergence_of_Stochastic_Global_Optimization_Algorithms.pdf | 745,52 kB | Adobe PDF | Ver/Abrir |