A shifted hyperbolic augmented Lagrangian-based artificial fish two swarm algorithm with guaranteed convergence for constrained global optimization
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/42943 |
Resumo: | This article presents a shifted hyperbolic penalty function and proposes an augmented Lagrangian-based algorithm for non-convex constrained global optimization problems. Convergence to an ε-global minimizer is proved. At each iteration k, the algorithm requires the ε(k)-global minimization of a bound constrained optimization subproblem, where ε(k) → ε. The subproblems are solved by a stochastic population-based metaheuristic that relies on the artificial fish swarm paradigm and a two-swarm strategy. To enhance the speed of convergence, the algorithm invokes the Nelder–Mead local search with a dynamically defined probability. Numerical experiments with benchmark functions and engineering design problems are presented. The results show that the proposed shifted hyperbolic augmented Lagrangian compares favorably with other deterministic and stochastic penalty-based methods. |
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A shifted hyperbolic augmented Lagrangian-based artificial fish two swarm algorithm with guaranteed convergence for constrained global optimizationGlobal optimizationAugmented LagrangianShifted hyperbolic penaltyArtificial fish swarmNelder–Mead searchEngenharia e Tecnologia::Outras Engenharias e TecnologiasCiências Naturais::MatemáticasScience & TechnologyThis article presents a shifted hyperbolic penalty function and proposes an augmented Lagrangian-based algorithm for non-convex constrained global optimization problems. Convergence to an ε-global minimizer is proved. At each iteration k, the algorithm requires the ε(k)-global minimization of a bound constrained optimization subproblem, where ε(k) → ε. The subproblems are solved by a stochastic population-based metaheuristic that relies on the artificial fish swarm paradigm and a two-swarm strategy. To enhance the speed of convergence, the algorithm invokes the Nelder–Mead local search with a dynamically defined probability. Numerical experiments with benchmark functions and engineering design problems are presented. The results show that the proposed shifted hyperbolic augmented Lagrangian compares favorably with other deterministic and stochastic penalty-based methods.This work was supported by COMPETE [POCI-01-0145-FEDER-007043]; FCT-Fundacao para a Ciencia e Tecnologia within the Project Scope [UID/CEC/00319/2013]; and partially supported by CMAT-Centre of Mathematics of the University of Minho.Taylor and FrancisUniversidade do MinhoRocha, Ana Maria A. C.Costa, M. Fernanda P.Fernandes, Edite Manuela da G. P.20162016-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/42943engAna Maria A.C. Rocha, M. Fernanda P. Costa & Edite M.G.P. Fernandes (2016) A shifted hyperbolic augmented Lagrangian-based artificial fish two-swarm algorithm with guaranteed convergence for constrained global optimization, Engineering Optimization, 48:12, 2114-2140, DOI: 10.1080/0305215X.2016.11576880305-215X10.1080/0305215X.2016.1157688http://www.tandfonline.com/info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:38:02Zoai:repositorium.sdum.uminho.pt:1822/42943Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:34:24.396658Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A shifted hyperbolic augmented Lagrangian-based artificial fish two swarm algorithm with guaranteed convergence for constrained global optimization |
title |
A shifted hyperbolic augmented Lagrangian-based artificial fish two swarm algorithm with guaranteed convergence for constrained global optimization |
spellingShingle |
A shifted hyperbolic augmented Lagrangian-based artificial fish two swarm algorithm with guaranteed convergence for constrained global optimization Rocha, Ana Maria A. C. Global optimization Augmented Lagrangian Shifted hyperbolic penalty Artificial fish swarm Nelder–Mead search Engenharia e Tecnologia::Outras Engenharias e Tecnologias Ciências Naturais::Matemáticas Science & Technology |
title_short |
A shifted hyperbolic augmented Lagrangian-based artificial fish two swarm algorithm with guaranteed convergence for constrained global optimization |
title_full |
A shifted hyperbolic augmented Lagrangian-based artificial fish two swarm algorithm with guaranteed convergence for constrained global optimization |
title_fullStr |
A shifted hyperbolic augmented Lagrangian-based artificial fish two swarm algorithm with guaranteed convergence for constrained global optimization |
title_full_unstemmed |
A shifted hyperbolic augmented Lagrangian-based artificial fish two swarm algorithm with guaranteed convergence for constrained global optimization |
title_sort |
A shifted hyperbolic augmented Lagrangian-based artificial fish two swarm algorithm with guaranteed convergence for constrained global optimization |
author |
Rocha, Ana Maria A. C. |
author_facet |
Rocha, Ana Maria A. C. Costa, M. Fernanda P. Fernandes, Edite Manuela da G. P. |
author_role |
author |
author2 |
Costa, M. Fernanda P. Fernandes, Edite Manuela da G. P. |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Rocha, Ana Maria A. C. Costa, M. Fernanda P. Fernandes, Edite Manuela da G. P. |
dc.subject.por.fl_str_mv |
Global optimization Augmented Lagrangian Shifted hyperbolic penalty Artificial fish swarm Nelder–Mead search Engenharia e Tecnologia::Outras Engenharias e Tecnologias Ciências Naturais::Matemáticas Science & Technology |
topic |
Global optimization Augmented Lagrangian Shifted hyperbolic penalty Artificial fish swarm Nelder–Mead search Engenharia e Tecnologia::Outras Engenharias e Tecnologias Ciências Naturais::Matemáticas Science & Technology |
description |
This article presents a shifted hyperbolic penalty function and proposes an augmented Lagrangian-based algorithm for non-convex constrained global optimization problems. Convergence to an ε-global minimizer is proved. At each iteration k, the algorithm requires the ε(k)-global minimization of a bound constrained optimization subproblem, where ε(k) → ε. The subproblems are solved by a stochastic population-based metaheuristic that relies on the artificial fish swarm paradigm and a two-swarm strategy. To enhance the speed of convergence, the algorithm invokes the Nelder–Mead local search with a dynamically defined probability. Numerical experiments with benchmark functions and engineering design problems are presented. The results show that the proposed shifted hyperbolic augmented Lagrangian compares favorably with other deterministic and stochastic penalty-based methods. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016 2016-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/42943 |
url |
http://hdl.handle.net/1822/42943 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ana Maria A.C. Rocha, M. Fernanda P. Costa & Edite M.G.P. Fernandes (2016) A shifted hyperbolic augmented Lagrangian-based artificial fish two-swarm algorithm with guaranteed convergence for constrained global optimization, Engineering Optimization, 48:12, 2114-2140, DOI: 10.1080/0305215X.2016.1157688 0305-215X 10.1080/0305215X.2016.1157688 http://www.tandfonline.com/ |
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 |
Taylor and Francis |
publisher.none.fl_str_mv |
Taylor and Francis |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799132865453096960 |