Novas Abordagens Sequencial e Paralela da meta-heurística C-GRASP Aplicadas à Otimização Global Contínua

• Ano de defesa: 2013
• Autor(a) principal: Andrade, Lisieux Marie Marinho dos Santos
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraí­ba; BR; Informática; Programa de Pós Graduação em Informática; UFPB
• Palavras-chave em Português: Otimização Global Contínua; Continuous Greedy Randomized Adaptive Search Procedure; C-GGVNS; Compute Unified Device Architecture; Continuous Global Optimization; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/tede/6095

The present work deals with the Continuous Global Optimization Problem, in its minimization form, by testing two approaches for the Continuous Greedy Randomized Adaptive Search Procedure (C-GRASP). The development of the first method - sequential and hybrid - comes from the deficiency of current approaches to provide a good neighborhood space exploration. Being constructed from the combination of two meta-heuristics, standard C-GRASP and Continuous General Variable Neighborhood Search (C-GVNS), as a strategy to achieving symmetric trades of neighborhood structures, it performed efficiently in the computational tests that were taken. The second procedure arises from the large consume of time when using high dimension functions with the standard C-GRASP construction procedure. As the optimization problems have a high dimensionality increase, it s preferable to have two parallel versions of the optimization method in order to handle bigger problems. Thus, for this new procedure developed, it was used the Compute Unified Device Architecture (CUDA), which provided promising acceleration regarding the processing time, based on the experiments performed.