Gaussian variable neighborhood search for continuous optimization

Variable Neighborhood Search (VNS) has shown to be a powerful tool for solving both discrete and box-constrained continuous optimization problems. In this note we extend the methodology by allowing also to address unconstrained continuous optimization problems. Instead of perturbing the incumbent so...

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Detalhes bibliográficos
Autores: Carrizosa Priego, Emilio José, Drazic, Milan, Drazic, Zorica, Mladenović, Nenad
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2012
País:España
Recursos:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/107639
Acesso em linha:https://hdl.handle.net/11441/107639
https://doi.org/10.1016/j.cor.2011.11.003
Access Level:acceso abierto
Palavra-chave:Global optimization
Nonlinear programming
Metaheuristics
Variable neighborhood search
Gaussian distribution
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spelling Gaussian variable neighborhood search for continuous optimizationCarrizosa Priego, Emilio JoséDrazic, MilanDrazic, ZoricaMladenović, NenadGlobal optimizationNonlinear programmingMetaheuristicsVariable neighborhood searchGaussian distributionVariable Neighborhood Search (VNS) has shown to be a powerful tool for solving both discrete and box-constrained continuous optimization problems. In this note we extend the methodology by allowing also to address unconstrained continuous optimization problems. Instead of perturbing the incumbent solution by randomly generating a trial point in a ball of a given metric, we propose to perturb the incumbent solution by adding some noise, following a Gaussian distribution. This way of generating new trial points allows one to give, in a simple and intuitive way, preference to some directions in the search space, or, contrarily, to treat uniformly all directions. Computational results show some advantages of this new approach.PERGAMON-ELSEVIER SCIENCE LTDEstadística e Investigación OperativaFQM329: Optimización2012info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/107639https://doi.org/10.1016/j.cor.2011.11.003reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésComputers & Operations Research, 39 (9), 2206-2213.https://doi.org/10.1016/j.cor.2011.11.003info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1076392026-06-17T12:51:07Z
dc.title.none.fl_str_mv Gaussian variable neighborhood search for continuous optimization
title Gaussian variable neighborhood search for continuous optimization
spellingShingle Gaussian variable neighborhood search for continuous optimization
Carrizosa Priego, Emilio José
Global optimization
Nonlinear programming
Metaheuristics
Variable neighborhood search
Gaussian distribution
title_short Gaussian variable neighborhood search for continuous optimization
title_full Gaussian variable neighborhood search for continuous optimization
title_fullStr Gaussian variable neighborhood search for continuous optimization
title_full_unstemmed Gaussian variable neighborhood search for continuous optimization
title_sort Gaussian variable neighborhood search for continuous optimization
dc.creator.none.fl_str_mv Carrizosa Priego, Emilio José
Drazic, Milan
Drazic, Zorica
Mladenović, Nenad
author Carrizosa Priego, Emilio José
author_facet Carrizosa Priego, Emilio José
Drazic, Milan
Drazic, Zorica
Mladenović, Nenad
author_role author
author2 Drazic, Milan
Drazic, Zorica
Mladenović, Nenad
author2_role author
author
author
dc.contributor.none.fl_str_mv Estadística e Investigación Operativa
FQM329: Optimización
dc.subject.none.fl_str_mv Global optimization
Nonlinear programming
Metaheuristics
Variable neighborhood search
Gaussian distribution
topic Global optimization
Nonlinear programming
Metaheuristics
Variable neighborhood search
Gaussian distribution
description Variable Neighborhood Search (VNS) has shown to be a powerful tool for solving both discrete and box-constrained continuous optimization problems. In this note we extend the methodology by allowing also to address unconstrained continuous optimization problems. Instead of perturbing the incumbent solution by randomly generating a trial point in a ball of a given metric, we propose to perturb the incumbent solution by adding some noise, following a Gaussian distribution. This way of generating new trial points allows one to give, in a simple and intuitive way, preference to some directions in the search space, or, contrarily, to treat uniformly all directions. Computational results show some advantages of this new approach.
publishDate 2012
dc.date.none.fl_str_mv 2012
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/107639
https://doi.org/10.1016/j.cor.2011.11.003
url https://hdl.handle.net/11441/107639
https://doi.org/10.1016/j.cor.2011.11.003
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Computers & Operations Research, 39 (9), 2206-2213.
https://doi.org/10.1016/j.cor.2011.11.003
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv PERGAMON-ELSEVIER SCIENCE LTD
publisher.none.fl_str_mv PERGAMON-ELSEVIER SCIENCE LTD
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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