1. Universidade Federal do Rio Grande do Norte
  2. Escola de Ciências e Tecnologia
  3. ECT - Artigos publicados em periódicos
Use este identificador para citar ou linkar para este item: https://repositorio.ufrn.br/handle/123456789/30939
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dc.contributor.authorFernandes, Islame Felipe da Costa-
dc.contributor.authorSilva, Igor Rosberg de Medeiros-
dc.contributor.authorGoldbarg, Elizabeth Ferreira Gouvea-
dc.contributor.authorMaia, Silvia Maria Diniz Monteiro-
dc.contributor.authorGoldbarg, Marco César-
dc.date.accessioned2020-12-09T17:23:42Z-
dc.date.available2020-12-09T17:23:42Z-
dc.date.issued2020-06-22-
dc.identifier.citationFERNANDES, I. F. C.; SILVA, I. R. M.; GOLDBARG, E. F. G.; MAIA, S. M. D. M.; GOLDBARG, M. C.. A PSO-inspired architecture to hybridise multi-objective metaheuristics. Memetic Computing, [S.L.], v. 12, n. 3, p. 235-249, 22 jun. 2020. Disponível em: https://link.springer.com/article/10.1007/s12293-020-00307-4. Acesso em: 07 out. 2020. http://dx.doi.org/10.1007/s12293-020-00307-4.pt_BR
dc.identifier.issn1865-9292-
dc.identifier.issn1865-9284-
dc.identifier.urihttps://repositorio.ufrn.br/handle/123456789/30939-
dc.languageenpt_BR
dc.publisherSpringerpt_BR
dc.subjectMulti-objective optimisationpt_BR
dc.subjectHybridisation of metaheuristicspt_BR
dc.subjectBi-objective spanning treept_BR
dc.titleA PSO-inspired architecture to hybridise multi-objective metaheuristicspt_BR
dc.typearticlept_BR
dc.identifier.doi10.1007/s12293-020-00307-4-
dc.description.resumoHybridisation is a technique that exploits and unites the best features of individual algorithms. The literature includes several hybridisation methodologies, among which there are general procedures, termed architectures, that provide generic functionalities and features for solving optimisation problems. Successful hybridisation methodologies have applied concepts of the multi-agent paradigm, such as cooperation and agent intelligence. However, there is still a lack concerning architectures for the hybridisation of multi-objective metaheuristics that fully explore these concepts. This study proposes a new architecture, named MO-MAHM, based on concepts from Particle Swarm Optimisation, to hybridise multi-objective metaheuristics. We apply the MO-MAHM to the Bi-objective Spanning Tree Problem. Four algorithms were hybridised within the MO-MAHM: three evolutionary algorithms and a local search method. We report the results of experiments with 180 instances, analyse the behaviour of the MO-MAHM, and compare to the results produced by algorithms proposed for the Bi-objective Spanning Tree Problempt_BR
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