A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture

Autores
Mroginski, Javier Luis; Castro, Hugo Guillermo
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
It is well known that the numerical solution of evolutionary systems and problems based on topological design requires a high computational power. In the last years, many parallel algorithms have been developed in order to improve its performance. Among them, genetic algorithms (GAs) are one of the most popular metaheuristic algorithms inspired by Darwin´s evolution theory. From the High Performance Computing (HPC) point of view, the CUDA environment is probably the parallel computing platform and programming model that more heyday has had in recent years, mainly due to the low acquisition cost of graphics processing units (GPUs) compared to a cluster with similar functional characteristics. Consequently, the number of GPU-CUDAs present in the top 500 fastest supercomputers in the world is constantly growing. In this paper, a numerical algorithm developed in the NVIDIA CUDA platform capable of solving classical optimization functions usually employed as benchmarks is presented. The obtained results demonstrate that GPUs are a valuable tool for acceleration of GAs and may enable its use in much complex problems. Also, a sensitivity analysis is carried out in order to show the relative weight of each GA operator in the whole computational cost of the algorithm.
Fil: Mroginski, Javier Luis. Universidad Nacional del Nordeste. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina
Fil: Castro, Hugo Guillermo. Universidad Tecnológica Nacional. Facultad Reg. Resistencia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; Argentina
Materia
CUDA ENVIRONMENT
GENETIC ALGORITHM
MATHEMATICAL FUNCTION OPTIMIZATION
GPU ARCHITECTURE
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/86659
Acceder
id CONICETDig_597b705e1124da0878750fc01f151ddb
oai_identifier_str oai:ri.conicet.gov.ar:11336/86659
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architectureMroginski, Javier LuisCastro, Hugo GuillermoCUDA ENVIRONMENTGENETIC ALGORITHMMATHEMATICAL FUNCTION OPTIMIZATIONGPU ARCHITECTUREhttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2It is well known that the numerical solution of evolutionary systems and problems based on topological design requires a high computational power. In the last years, many parallel algorithms have been developed in order to improve its performance. Among them, genetic algorithms (GAs) are one of the most popular metaheuristic algorithms inspired by Darwin´s evolution theory. From the High Performance Computing (HPC) point of view, the CUDA environment is probably the parallel computing platform and programming model that more heyday has had in recent years, mainly due to the low acquisition cost of graphics processing units (GPUs) compared to a cluster with similar functional characteristics. Consequently, the number of GPU-CUDAs present in the top 500 fastest supercomputers in the world is constantly growing. In this paper, a numerical algorithm developed in the NVIDIA CUDA platform capable of solving classical optimization functions usually employed as benchmarks is presented. The obtained results demonstrate that GPUs are a valuable tool for acceleration of GAs and may enable its use in much complex problems. Also, a sensitivity analysis is carried out in order to show the relative weight of each GA operator in the whole computational cost of the algorithm.Fil: Mroginski, Javier Luis. Universidad Nacional del Nordeste. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; ArgentinaFil: Castro, Hugo Guillermo. Universidad Tecnológica Nacional. Facultad Reg. Resistencia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; ArgentinaEmrah Evren Kara2018-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/86659Mroginski, Javier Luis; Castro, Hugo Guillermo; A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture; Emrah Evren Kara; Communications in Advanced Mathematical Sciences; 1; 1; 9-2018; 67-832651-4001CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://dergipark.gov.tr/cams/issue/39351/459423info:eu-repo/semantics/altIdentifier/doi/10.33434/cams.459423info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:52:06Zoai:ri.conicet.gov.ar:11336/86659instacron: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 09:52:06.514CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture
title A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture
spellingShingle A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture
Mroginski, Javier Luis
CUDA ENVIRONMENT
GENETIC ALGORITHM
MATHEMATICAL FUNCTION OPTIMIZATION
GPU ARCHITECTURE
title_short A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture
title_full A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture
title_fullStr A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture
title_full_unstemmed A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture
title_sort A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture
dc.creator.none.fl_str_mv Mroginski, Javier Luis
Castro, Hugo Guillermo
author Mroginski, Javier Luis
author_facet Mroginski, Javier Luis
Castro, Hugo Guillermo
author_role author
author2 Castro, Hugo Guillermo
author2_role author
dc.subject.none.fl_str_mv CUDA ENVIRONMENT
GENETIC ALGORITHM
MATHEMATICAL FUNCTION OPTIMIZATION
GPU ARCHITECTURE
topic CUDA ENVIRONMENT
GENETIC ALGORITHM
MATHEMATICAL FUNCTION OPTIMIZATION
GPU ARCHITECTURE
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv It is well known that the numerical solution of evolutionary systems and problems based on topological design requires a high computational power. In the last years, many parallel algorithms have been developed in order to improve its performance. Among them, genetic algorithms (GAs) are one of the most popular metaheuristic algorithms inspired by Darwin´s evolution theory. From the High Performance Computing (HPC) point of view, the CUDA environment is probably the parallel computing platform and programming model that more heyday has had in recent years, mainly due to the low acquisition cost of graphics processing units (GPUs) compared to a cluster with similar functional characteristics. Consequently, the number of GPU-CUDAs present in the top 500 fastest supercomputers in the world is constantly growing. In this paper, a numerical algorithm developed in the NVIDIA CUDA platform capable of solving classical optimization functions usually employed as benchmarks is presented. The obtained results demonstrate that GPUs are a valuable tool for acceleration of GAs and may enable its use in much complex problems. Also, a sensitivity analysis is carried out in order to show the relative weight of each GA operator in the whole computational cost of the algorithm.
Fil: Mroginski, Javier Luis. Universidad Nacional del Nordeste. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina
Fil: Castro, Hugo Guillermo. Universidad Tecnológica Nacional. Facultad Reg. Resistencia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; Argentina
description It is well known that the numerical solution of evolutionary systems and problems based on topological design requires a high computational power. In the last years, many parallel algorithms have been developed in order to improve its performance. Among them, genetic algorithms (GAs) are one of the most popular metaheuristic algorithms inspired by Darwin´s evolution theory. From the High Performance Computing (HPC) point of view, the CUDA environment is probably the parallel computing platform and programming model that more heyday has had in recent years, mainly due to the low acquisition cost of graphics processing units (GPUs) compared to a cluster with similar functional characteristics. Consequently, the number of GPU-CUDAs present in the top 500 fastest supercomputers in the world is constantly growing. In this paper, a numerical algorithm developed in the NVIDIA CUDA platform capable of solving classical optimization functions usually employed as benchmarks is presented. The obtained results demonstrate that GPUs are a valuable tool for acceleration of GAs and may enable its use in much complex problems. Also, a sensitivity analysis is carried out in order to show the relative weight of each GA operator in the whole computational cost of the algorithm.
publishDate 2018
dc.date.none.fl_str_mv 2018-09
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/11336/86659
Mroginski, Javier Luis; Castro, Hugo Guillermo; A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture; Emrah Evren Kara; Communications in Advanced Mathematical Sciences; 1; 1; 9-2018; 67-83
2651-4001
CONICET Digital
CONICET
url http://hdl.handle.net/11336/86659
identifier_str_mv Mroginski, Javier Luis; Castro, Hugo Guillermo; A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture; Emrah Evren Kara; Communications in Advanced Mathematical Sciences; 1; 1; 9-2018; 67-83
2651-4001
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://dergipark.gov.tr/cams/issue/39351/459423
info:eu-repo/semantics/altIdentifier/doi/10.33434/cams.459423
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Emrah Evren Kara
publisher.none.fl_str_mv Emrah Evren Kara
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
_version_ 1842269135895003136
score 13.13397

Publicaciones similares