Please use this identifier to cite or link to this item: http://carpedien.ien.gov.br:8080/handle/ien/1693
Tipo: conferenceObject
Título: The Particle Swarm Optimization Algorithm Applied to Nuclear Systems Surveillance Test Planning
Autor(es): SIQUEIRA, Newton Norat
PEREIRA, Cláudio Márcio do Nascimento Abreu
LAPA, Celso Marcelo Franklin
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.
Palavras-chave: Particle Swarm Optimization
Nuclear Power Plant
Genetic Algorithms
Idioma: por
País: Brasil
Editor: Instituto de Engenharia Nuclear
Sigla da Instituição: IEN
Tipo de Acesso: openAccess
URI: http://carpedien.ien.gov.br:8080/handle/ien/1693
Data do documento: 2005
Appears in Collections:Desenvolvimento de Tecnologia para Sistemas Complexos - Trabalhos de Congresso

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