Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/11361
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dc.contributor.authorSilva, Maria Amélia Lopes-
dc.contributor.authorSouza, Sergio Ricardo de-
dc.contributor.authorSouza, Marcone Jamilson Freitas-
dc.contributor.authorFrança Filho, Moacir Felizardo de-
dc.date.accessioned2019-06-03T13:31:50Z-
dc.date.available2019-06-03T13:31:50Z-
dc.date.issued2018-
dc.identifier.citationSILVA, M. A. L. et al. Hybrid metaheuristics and multi-agent systems for solving optimization problems : a review of frameworks and a comparative analysis. Applied Soft Computing, v. 71, p. 433-459, out. 2018. Disponível em: <https://www.sciencedirect.com/science/article/pii/S1568494618303867>. Acesso em: 19 mar. 2019.pt_BR
dc.identifier.issn1568-4946-
dc.identifier.urihttp://www.repositorio.ufop.br/handle/123456789/11361-
dc.description.abstractThis article presents a review and a comparative analysis between frameworks for solving optimization problems using metaheuristics. The aim is to identify both the desirable characteristics as the existing gaps in the current state of the art, with a special focus on the use of multi-agent structures in the development of hybrid metaheuristics. A literature review of existing frameworks is introduced, with emphasis on their characteristics of hybridization, cooperation, and parallelism, particularly focusing on issues related to the use of multi-agents. For the comparative analysis, a set of twenty-two characteristics was listed, according to four categories: basics, advanced, multi-agent approach and support to the optimization process. Strategies used in hybridization, such as parallelism, cooperation, decomposition of the search space, hyper-heuristic and multi-agent systems are assessed in respect to their use in the various analyzed frameworks. Specific features of multi-agent systems, such as learning and interaction between agents, are also analyzed. The comparative analysis shows that the hybridization is not a strong feature in existing frameworks. On the other hand, proposals using multi-agent systems stand out in the implementation of hybrid methods, as they allow the interaction between metaheuristics. It also notes that the concept of hyper-heuristic is little explored by the analyzed frameworks, as well as there is a lack of tools that offer support to the optimization process, such as statistical analysis, self-tuning of parameters and graphical interfaces. Based on the presented analysis, it can be said that there are important gaps to be filled in the development of Frameworks for Optimization using metaheuristics, which open important possibilities for future works, particularly by implementing the approach of multi-agent systems.pt_BR
dc.language.isoen_USpt_BR
dc.rightsrestritopt_BR
dc.subjectCooperationpt_BR
dc.subjectCombinatorial optimizationpt_BR
dc.subjectHybridizationpt_BR
dc.titleHybrid metaheuristics and multi-agent systems for solving optimization problems : a review of frameworks and a comparative analysis.pt_BR
dc.typeArtigo publicado em periodicopt_BR
dc.identifier.uri2https://www.sciencedirect.com/science/article/pii/S1568494618303867pt_BR
dc.identifier.doihttps://doi.org/10.1016/j.asoc.2018.06.050pt_BR
Appears in Collections:DECOM - Artigos publicados em periódicos

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