The Particle Swarm Optimization Algorithm Applied to Nuclear Systems Surveillance Test Planning

Detalhes bibliográficos
Autor(a) principal: SIQUEIRA, Newton Norat
Data de Publicação: 2005
Outros Autores: PEREIRA, Cláudio Márcio do Nascimento Abreu, LAPA, Celso Marcelo Franklin, norat@click21.com.br, lapa@cnen.gov.br, cmnap@ien.gov.br
Tipo de documento: Artigo de conferência
Idioma: por
Título da fonte: Repositório Institucional do IEN
Texto Completo: http://carpedien.ien.gov.br:8080/handle/ien/1693
Resumo: This work presents Particle Swarm Optimization (PSO) as an alternative method for optimizing surveillance test policies in nuclear power plant (NPP) electromechanical systems, which has been successfully handled by the use of Genetic Algorithms (GA). The main idea is to find the optimum interval between test interventions, for each component of the system, considering as main objective, the system’s average availability, during a given time period. Computational experiments demonstrated that PSO was able to find optimized surveillance test policies. In the case study used in this work, PSO has outperformed the GA, achieving slightly better results, with lower computational efforts.
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spelling SIQUEIRA, Newton NoratPEREIRA, Cláudio Márcio do Nascimento AbreuLAPA, Celso Marcelo Franklinnorat@click21.com.brlapa@cnen.gov.brcmnap@ien.gov.br2016-04-19T14:34:17Z2016-04-19T14:34:17Z20052005http://carpedien.ien.gov.br:8080/handle/ien/1693Submitted by Sherillyn Lopes (sherillynmartins@yahoo.com.br) on 2016-04-19T14:34:17Z No. of bitstreams: 1 The particle swarm optimization algorithm.pdf: 76687 bytes, checksum: ddc6cfe8de35f5f0e1f49cc1000e940b (MD5)Made available in DSpace on 2016-04-19T14:34:17Z (GMT). No. of bitstreams: 1 The particle swarm optimization algorithm.pdf: 76687 bytes, checksum: ddc6cfe8de35f5f0e1f49cc1000e940b (MD5) Previous issue date: 2005This work presents Particle Swarm Optimization (PSO) as an alternative method for optimizing surveillance test policies in nuclear power plant (NPP) electromechanical systems, which has been successfully handled by the use of Genetic Algorithms (GA). The main idea is to find the optimum interval between test interventions, for each component of the system, considering as main objective, the system’s average availability, during a given time period. Computational experiments demonstrated that PSO was able to find optimized surveillance test policies. In the case study used in this work, PSO has outperformed the GA, achieving slightly better results, with lower computational efforts.porInstituto de Engenharia NuclearIENBrasilParticle Swarm OptimizationNuclear Power PlantGenetic AlgorithmsThe Particle Swarm Optimization Algorithm Applied to Nuclear Systems Surveillance Test Planninginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject2005info:eu-repo/semantics/openAccessreponame:Repositório Institucional do IENinstname:Instituto de Engenharia Nuclearinstacron:IENLICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://carpedien.ien.gov.br:8080/xmlui/bitstream/ien/1693/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52ORIGINALThe particle swarm optimization algorithm.pdfThe particle swarm optimization algorithm.pdfapplication/pdf76687http://carpedien.ien.gov.br:8080/xmlui/bitstream/ien/1693/1/The+particle+swarm+optimization+algorithm.pdfddc6cfe8de35f5f0e1f49cc1000e940bMD51ien/1693oai:carpedien.ien.gov.br:ien/16932016-04-19 11:34:17.04Dspace IENlsales@ien.gov.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
dc.title.pt_BR.fl_str_mv The Particle Swarm Optimization Algorithm Applied to Nuclear Systems Surveillance Test Planning
title The Particle Swarm Optimization Algorithm Applied to Nuclear Systems Surveillance Test Planning
spellingShingle The Particle Swarm Optimization Algorithm Applied to Nuclear Systems Surveillance Test Planning
SIQUEIRA, Newton Norat
Particle Swarm Optimization
Nuclear Power Plant
Genetic Algorithms
title_short The Particle Swarm Optimization Algorithm Applied to Nuclear Systems Surveillance Test Planning
title_full The Particle Swarm Optimization Algorithm Applied to Nuclear Systems Surveillance Test Planning
title_fullStr The Particle Swarm Optimization Algorithm Applied to Nuclear Systems Surveillance Test Planning
title_full_unstemmed The Particle Swarm Optimization Algorithm Applied to Nuclear Systems Surveillance Test Planning
title_sort The Particle Swarm Optimization Algorithm Applied to Nuclear Systems Surveillance Test Planning
author SIQUEIRA, Newton Norat
author_facet SIQUEIRA, Newton Norat
PEREIRA, Cláudio Márcio do Nascimento Abreu
LAPA, Celso Marcelo Franklin
norat@click21.com.br
lapa@cnen.gov.br
cmnap@ien.gov.br
author_role author
author2 PEREIRA, Cláudio Márcio do Nascimento Abreu
LAPA, Celso Marcelo Franklin
norat@click21.com.br
lapa@cnen.gov.br
cmnap@ien.gov.br
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv SIQUEIRA, Newton Norat
PEREIRA, Cláudio Márcio do Nascimento Abreu
LAPA, Celso Marcelo Franklin
norat@click21.com.br
lapa@cnen.gov.br
cmnap@ien.gov.br
dc.subject.por.fl_str_mv Particle Swarm Optimization
Nuclear Power Plant
Genetic Algorithms
topic Particle Swarm Optimization
Nuclear Power Plant
Genetic Algorithms
dc.description.abstract.por.fl_txt_mv This work presents Particle Swarm Optimization (PSO) as an alternative method for optimizing surveillance test policies in nuclear power plant (NPP) electromechanical systems, which has been successfully handled by the use of Genetic Algorithms (GA). The main idea is to find the optimum interval between test interventions, for each component of the system, considering as main objective, the system’s average availability, during a given time period. Computational experiments demonstrated that PSO was able to find optimized surveillance test policies. In the case study used in this work, PSO has outperformed the GA, achieving slightly better results, with lower computational efforts.
description This work presents Particle Swarm Optimization (PSO) as an alternative method for optimizing surveillance test policies in nuclear power plant (NPP) electromechanical systems, which has been successfully handled by the use of Genetic Algorithms (GA). The main idea is to find the optimum interval between test interventions, for each component of the system, considering as main objective, the system’s average availability, during a given time period. Computational experiments demonstrated that PSO was able to find optimized surveillance test policies. In the case study used in this work, PSO has outperformed the GA, achieving slightly better results, with lower computational efforts.
publishDate 2005
dc.date.copyright.none.fl_str_mv 2005
dc.date.issued.fl_str_mv 2005
dc.date.accessioned.fl_str_mv 2016-04-19T14:34:17Z
dc.date.available.fl_str_mv 2016-04-19T14:34:17Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
status_str publishedVersion
format conferenceObject
dc.identifier.uri.fl_str_mv http://carpedien.ien.gov.br:8080/handle/ien/1693
url http://carpedien.ien.gov.br:8080/handle/ien/1693
dc.language.iso.fl_str_mv por
language por
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Instituto de Engenharia Nuclear
dc.publisher.initials.fl_str_mv IEN
dc.publisher.country.fl_str_mv Brasil
publisher.none.fl_str_mv Instituto de Engenharia Nuclear
dc.source.none.fl_str_mv reponame:Repositório Institucional do IEN
instname:Instituto de Engenharia Nuclear
instacron:IEN
reponame_str Repositório Institucional do IEN
collection Repositório Institucional do IEN
instname_str Instituto de Engenharia Nuclear
instacron_str IEN
institution IEN
bitstream.url.fl_str_mv http://carpedien.ien.gov.br:8080/xmlui/bitstream/ien/1693/2/license.txt
http://carpedien.ien.gov.br:8080/xmlui/bitstream/ien/1693/1/The+particle+swarm+optimization+algorithm.pdf
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repository.name.fl_str_mv Dspace IEN
repository.mail.fl_str_mv lsales@ien.gov.br
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