Meta-heurísticas híbridas aplicadas ao problema da árvore geradora multiobjetivo

The Multi-objective Spanning Tree Problem (MSTP) is an NP-hard extension of the Minimum Spanning Tree (MST). Once the MTSP models several real-world problems in which conicting objectives need to be optimized simultaneously, it has been extensively studied in the literature and several exact and heu...

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Detalhes bibliográficos
Autor: Fernandes, Islame Felipe da Costa
Tipo de documento: dissertação
Estado:Versão publicada
Data de publicação:2018
País:Brasil
Recursos:Universidade Federal do Rio Grande do Norte (UFRN)
Repositório:Repositório Institucional da UFRN
Idioma:português
OAI Identifier:oai:repositorio.ufrn.br:123456789/25660
Acesso em linha:https://repositorio.ufrn.br/jspui/handle/123456789/25660
Access Level:Acceso aberto
Palavra-chave:Árvore geradora multiobjetivo
Meta-heurísticas híbridas
Operador OWA
Algoritmos experimentais
CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
Descrição
Resumo:The Multi-objective Spanning Tree Problem (MSTP) is an NP-hard extension of the Minimum Spanning Tree (MST). Once the MTSP models several real-world problems in which conicting objectives need to be optimized simultaneously, it has been extensively studied in the literature and several exact and heuristic algorithms were proposed for it. Besides, over the last years, researchs have showed the considerable performance of algorithms that combine various metaheuristic strategies. They are called hybrid algorithms and previous works successfully applied them to several optimization problems. In this work, five new hybrid algorithms are proposed for two versions of the MSTP: three for the bi-objective version (BiST) based on Pareto dominance and two for the manyobjective version based on the ordered weighted average operator (OWA-ST). This research hybridized elements from various metaheuristics. Computational experiments investigated the potential of the new algorithms concerning computational time and solution quality. The results were compared to the state-of-the-art.