An augmented lagrangian fish swarm based method for global optimization

Detalhes bibliográficos
Autor(a) principal: Rocha, Ana Maria A. C.
Data de Publicação: 2011
Outros Autores: Martins, Tiago F. M. C., Fernandes, Edite Manuela da G. P.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/1822/12909
Resumo: This paper presents an augmented Lagrangian methodology with a stochastic population based algorithm for solving nonlinear constrained global optimization problems. The method approximately solves a sequence of simple bound global optimization subproblems using a fish swarm intelligent algorithm. A stochastic convergence analysis of the fish swarm iterative process is included. Numerical results with a benchmark set of problems are shown, including a comparison with other stochastic-type algorithms.
id RCAP_87daec3dbfa954996d74ad21aacea9d5
oai_identifier_str oai:repositorium.sdum.uminho.pt:1822/12909
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling An augmented lagrangian fish swarm based method for global optimizationAugmented Lagrangian functionArtificial fish swarmStochastic convergenceScience & TechnologyThis paper presents an augmented Lagrangian methodology with a stochastic population based algorithm for solving nonlinear constrained global optimization problems. The method approximately solves a sequence of simple bound global optimization subproblems using a fish swarm intelligent algorithm. A stochastic convergence analysis of the fish swarm iterative process is included. Numerical results with a benchmark set of problems are shown, including a comparison with other stochastic-type algorithms.Fundação para a Ciência e a Tecnologia (FCT)Elsevier ScienceUniversidade do MinhoRocha, Ana Maria A. C.Martins, Tiago F. M. C.Fernandes, Edite Manuela da G. P.2011-062011-06-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/12909eng0377-042710.1016/j.cam.2010.04.020http://www.sciencedirect.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:RCAAP2023-07-21T12:41:27Zoai:repositorium.sdum.uminho.pt:1822/12909Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:38:26.455600Repositório 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 An augmented lagrangian fish swarm based method for global optimization
title An augmented lagrangian fish swarm based method for global optimization
spellingShingle An augmented lagrangian fish swarm based method for global optimization
Rocha, Ana Maria A. C.
Augmented Lagrangian function
Artificial fish swarm
Stochastic convergence
Science & Technology
title_short An augmented lagrangian fish swarm based method for global optimization
title_full An augmented lagrangian fish swarm based method for global optimization
title_fullStr An augmented lagrangian fish swarm based method for global optimization
title_full_unstemmed An augmented lagrangian fish swarm based method for global optimization
title_sort An augmented lagrangian fish swarm based method for global optimization
author Rocha, Ana Maria A. C.
author_facet Rocha, Ana Maria A. C.
Martins, Tiago F. M. C.
Fernandes, Edite Manuela da G. P.
author_role author
author2 Martins, Tiago F. M. C.
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.
Martins, Tiago F. M. C.
Fernandes, Edite Manuela da G. P.
dc.subject.por.fl_str_mv Augmented Lagrangian function
Artificial fish swarm
Stochastic convergence
Science & Technology
topic Augmented Lagrangian function
Artificial fish swarm
Stochastic convergence
Science & Technology
description This paper presents an augmented Lagrangian methodology with a stochastic population based algorithm for solving nonlinear constrained global optimization problems. The method approximately solves a sequence of simple bound global optimization subproblems using a fish swarm intelligent algorithm. A stochastic convergence analysis of the fish swarm iterative process is included. Numerical results with a benchmark set of problems are shown, including a comparison with other stochastic-type algorithms.
publishDate 2011
dc.date.none.fl_str_mv 2011-06
2011-06-01T00:00:00Z
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/1822/12909
url http://hdl.handle.net/1822/12909
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0377-0427
10.1016/j.cam.2010.04.020
http://www.sciencedirect.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 Elsevier Science
publisher.none.fl_str_mv Elsevier Science
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
_version_ 1799132921751142400

Registros relacionados