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dc.contributor.authorYang, Shengxiangen
dc.contributor.authorJiang, Y.
dc.contributor.authorNguyen, T. T.
dc.date.accessioned2013-05-15T12:59:22Z
dc.date.available2013-05-15T12:59:22Z
dc.date.issued2012
dc.identifier.citationYang, S., Jiang, Y. and Nguyen, T. (2012) Metaheuristics for dynamic combinatorial optimization problems. IMA Journal of Management Mathematics, 24 (4), pp. 451-480en
dc.identifier.issn1471-6798
dc.identifier.urihttp://hdl.handle.net/2086/8496
dc.description.abstractMany real-world optimization problems are combinatorial optimization problems subject to dynamic environments. In such dynamic combinatorial optimization problems (DCOPs), the objective, decision variables and/or constraints may change over time, and so solving DCOPs is a challenging task. Metaheuristics are a good choice of tools to tackle DCOPs because many metaheuristics are inspired by natural or biological evolution processes, which are always subject to changing environments. In recent years, DCOPs have attracted a growing interest from the metaheuristics community. This paper is a tutorial on metaheuristics for DCOPs. We cover the definition of DCOPs, typical benchmark problems and their characteristics, methodologies and performance measures, real-world case study and key challenges in the area. Some future research directions are also pointed out in this paper.en
dc.language.isoenen
dc.publisherThe Institute of Mathematics and its Applications.en
dc.subjectMetaheuristicsen
dc.subjectGenetic algorithmen
dc.subjectAnt colony optimizationen
dc.subjectDynamic optimization problemen
dc.subjectDynamic combinatorial optimization problemen
dc.titleMetaheuristics for dynamic combinatorial optimization problems.en
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1093/imaman/dps021
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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