Estratégias de exploração de vizinhança com GPU para problemas de otimização

• Autor(a) principal: Araújo, Rodolfo Pereira
• Data de Publicação: 2018
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UERJ
• Texto Completo: http://www.bdtd.uerj.br/handle/1/7658
• topic: Meta-heuristics; Local Search; Dataflow; Graphics Processing Unit; Variable Neighborhood Descent; Meta-heurística; Busca Local; Dataflow; Graphics Processing Unit; Vari able Neighborhood Descent; Programação (Computadores); Processamento eletrônico de dados; Heurística; Otimização matemática; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

Optimization problems have big importance in the industry field, from production management to production outflow and product transportation. Many problems of interest are classified as NP-Hard, so there is no known algorithm to find the exact solution in a polinomial time. Therefore heuristic strategies with the ability to escape from poor quality local optima (meta-heuristics) are generally employed. In general, the local search is the most costly, in computational time, phase of a meta-heuristic, becoming mandatory a good use of the available resources. The parallel processing of neighborhood strategies is implemented at the fine grain level through GPU processing and coarse grain through multi-core processing and network processing, the combination of the two level parallelization in a heterogeneous environment for von Neumann architectures and dataflow