On local convergence of stochastic global optimization algorithms

Detalhes bibliográficos
Autor(a) principal: Hendrix, Eligius M. T.
Data de Publicação: 2021
Outros Autores: Rocha, Ana Maria A. C.
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: https://hdl.handle.net/1822/78199
Resumo: 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.
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spelling On local convergence of stochastic global optimization algorithmsStochastic global optimizationEvolutionary algorithmsConvergenceNonlinear optimizationScience & TechnologyIn 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.- 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.SpringerUniversidade do MinhoHendrix, Eligius M. T.Rocha, Ana Maria A. C.20212021-01-01T00:00:00Zconference paperinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/1822/78199eng978-3-030-86975-50302-974310.1007/978-3-030-86976-2_31978-3-030-86976-2https://link.springer.com/chapter/10.1007/978-3-030-86976-2_31info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-11T07:00:06Zoai:repositorium.sdum.uminho.pt:1822/78199Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T16:11:35.242574Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv On local convergence of stochastic global optimization algorithms
title On local convergence of stochastic global optimization algorithms
spellingShingle On local convergence of stochastic global optimization algorithms
Hendrix, Eligius M. T.
Stochastic global optimization
Evolutionary algorithms
Convergence
Nonlinear optimization
Science & Technology
title_short On local convergence of stochastic global optimization algorithms
title_full On local convergence of stochastic global optimization algorithms
title_fullStr On local convergence of stochastic global optimization algorithms
title_full_unstemmed On local convergence of stochastic global optimization algorithms
title_sort On local convergence of stochastic global optimization algorithms
author Hendrix, Eligius M. T.
author_facet Hendrix, Eligius M. T.
Rocha, Ana Maria A. C.
author_role author
author2 Rocha, Ana Maria A. C.
author2_role author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Hendrix, Eligius M. T.
Rocha, Ana Maria A. C.
dc.subject.por.fl_str_mv Stochastic global optimization
Evolutionary algorithms
Convergence
Nonlinear optimization
Science & Technology
topic Stochastic global optimization
Evolutionary algorithms
Convergence
Nonlinear optimization
Science & Technology
description 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.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-01-01T00:00:00Z
dc.type.driver.fl_str_mv conference paper
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/1822/78199
url https://hdl.handle.net/1822/78199
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-3-030-86975-5
0302-9743
10.1007/978-3-030-86976-2_31
978-3-030-86976-2
https://link.springer.com/chapter/10.1007/978-3-030-86976-2_31
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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