Otimização por Nuvem de Partículas e Busca Tabu para Problema da Diversidade Máxima

• Ano de defesa: 2017
• Autor(a) principal: Bonotto, Edison Luiz
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Informática; Programa de Pós-Graduação em Informática; UFPB
• Palavras-chave em Português: Problema da Diversidade Máxima (MDP); Otimização por Nuvem de Partículas (PSO); Busca Tabu (TS); Otimização Combinatória; Problema NP-Difícil; Maximum Diversity Problem (MDP); Particle Swarm Optimization (PSO); Tabu Search (TS); Combinatorial Optimization; NP-Hard Problem; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/tede/9036

The Maximu m Diversity Problem (MDP) is a problem of combinatorial optimization area that aims to select a pre-set number of elements in a given set so that a sum of the differences between the selected elements are greater as possible. MDP belongs to the class of NP-Hard problems, that is, there is no known algorithm that solves in polynomial time accurately. Because they have a complexity of exponential order, require efficient heuristics to provide satisfactory results in acceptable time. However, heuristics do not guarantee the optimality of the solution found. This paper proposes a new hybrid approach for a resolution of the Maximum Diversity Problem and is based on the Particle Swarm Optimization (PSO) and Tabu Search (TS) metaheuristics, The algorithm is called PSO_TS. The use of PSO_TS achieves the best results for known instances testing in the literature, thus demonstrating be competitive with the best algorithms in terms of quality of the solutions.