Adapting distributed evolutionary algorithms to heterogeneous hardware

Autores
Salto, Carolina; Alba, Enrique
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Distributed computing environments are nowadays composed of many heterogeneous computers able to work cooperatively. Despite this, the most of the work in parallel metaheuristics assumes a homogeneous hardware as the underlying platform. In this work we provide a methodology that enables a distributed genetic algorithm to be customized for higher efficiency on any available hardware resources with different computing power, all of them collaborating to solve the same problem. We analyze the impact of heterogeneity in the resulting performance of a parallel metaheuristic and also its efficiency in time. Our conclusion is that the solution quality is comparable to that achieved by using a theoretically faster homogeneous platform, the traditional environment to execute this kind of algorithms, but an interesting finding is that those solutions are found with a lower numerical effort and even in lower running times in some cases.
Fil: Salto, Carolina. Universidad Nacional de la Pampa. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Alba, Enrique. Universidad de Málaga; España
Materia
distributed computing
heterogeneity
parallel algorithms
metaheuristics
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/114217
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Adapting distributed evolutionary algorithms to heterogeneous hardwareSalto, CarolinaAlba, Enriquedistributed computingheterogeneityparallel algorithmsmetaheuristicshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Distributed computing environments are nowadays composed of many heterogeneous computers able to work cooperatively. Despite this, the most of the work in parallel metaheuristics assumes a homogeneous hardware as the underlying platform. In this work we provide a methodology that enables a distributed genetic algorithm to be customized for higher efficiency on any available hardware resources with different computing power, all of them collaborating to solve the same problem. We analyze the impact of heterogeneity in the resulting performance of a parallel metaheuristic and also its efficiency in time. Our conclusion is that the solution quality is comparable to that achieved by using a theoretically faster homogeneous platform, the traditional environment to execute this kind of algorithms, but an interesting finding is that those solutions are found with a lower numerical effort and even in lower running times in some cases.Fil: Salto, Carolina. Universidad Nacional de la Pampa. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Alba, Enrique. Universidad de Málaga; EspañaSpringer2015-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/114217Salto, Carolina; Alba, Enrique; Adapting distributed evolutionary algorithms to heterogeneous hardware; Springer; Transactions on Computational Collective Intelligence; 19; 12-2015; 103-1252190-9288CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://link.springer.com/chapter/10.1007/978-3-662-49017-4_7info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-662-49017-4_7info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:55:45Zoai:ri.conicet.gov.ar:11336/114217instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:55:45.443CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Adapting distributed evolutionary algorithms to heterogeneous hardware
title Adapting distributed evolutionary algorithms to heterogeneous hardware
spellingShingle Adapting distributed evolutionary algorithms to heterogeneous hardware
Salto, Carolina
distributed computing
heterogeneity
parallel algorithms
metaheuristics
title_short Adapting distributed evolutionary algorithms to heterogeneous hardware
title_full Adapting distributed evolutionary algorithms to heterogeneous hardware
title_fullStr Adapting distributed evolutionary algorithms to heterogeneous hardware
title_full_unstemmed Adapting distributed evolutionary algorithms to heterogeneous hardware
title_sort Adapting distributed evolutionary algorithms to heterogeneous hardware
dc.creator.none.fl_str_mv Salto, Carolina
Alba, Enrique
author Salto, Carolina
author_facet Salto, Carolina
Alba, Enrique
author_role author
author2 Alba, Enrique
author2_role author
dc.subject.none.fl_str_mv distributed computing
heterogeneity
parallel algorithms
metaheuristics
topic distributed computing
heterogeneity
parallel algorithms
metaheuristics
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Distributed computing environments are nowadays composed of many heterogeneous computers able to work cooperatively. Despite this, the most of the work in parallel metaheuristics assumes a homogeneous hardware as the underlying platform. In this work we provide a methodology that enables a distributed genetic algorithm to be customized for higher efficiency on any available hardware resources with different computing power, all of them collaborating to solve the same problem. We analyze the impact of heterogeneity in the resulting performance of a parallel metaheuristic and also its efficiency in time. Our conclusion is that the solution quality is comparable to that achieved by using a theoretically faster homogeneous platform, the traditional environment to execute this kind of algorithms, but an interesting finding is that those solutions are found with a lower numerical effort and even in lower running times in some cases.
Fil: Salto, Carolina. Universidad Nacional de la Pampa. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Alba, Enrique. Universidad de Málaga; España
description Distributed computing environments are nowadays composed of many heterogeneous computers able to work cooperatively. Despite this, the most of the work in parallel metaheuristics assumes a homogeneous hardware as the underlying platform. In this work we provide a methodology that enables a distributed genetic algorithm to be customized for higher efficiency on any available hardware resources with different computing power, all of them collaborating to solve the same problem. We analyze the impact of heterogeneity in the resulting performance of a parallel metaheuristic and also its efficiency in time. Our conclusion is that the solution quality is comparable to that achieved by using a theoretically faster homogeneous platform, the traditional environment to execute this kind of algorithms, but an interesting finding is that those solutions are found with a lower numerical effort and even in lower running times in some cases.
publishDate 2015
dc.date.none.fl_str_mv 2015-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/114217
Salto, Carolina; Alba, Enrique; Adapting distributed evolutionary algorithms to heterogeneous hardware; Springer; Transactions on Computational Collective Intelligence; 19; 12-2015; 103-125
2190-9288
CONICET Digital
CONICET
url http://hdl.handle.net/11336/114217
identifier_str_mv Salto, Carolina; Alba, Enrique; Adapting distributed evolutionary algorithms to heterogeneous hardware; Springer; Transactions on Computational Collective Intelligence; 19; 12-2015; 103-125
2190-9288
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/chapter/10.1007/978-3-662-49017-4_7
info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-662-49017-4_7
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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