Hybrid metaheuristics : an automated approach

dc.contributor.authorHassan, Ahmed
dc.contributor.authorPillay, Nelishia
dc.contributor.emailnpillay@cs.up.ac.zaen_ZA
dc.date.accessioned2019-08-14T07:38:32Z
dc.date.issued2019-09
dc.description.abstractHybrid metaheuristics have proven to be effective at solving complex real-world problems. However, designing hybrid metaheuristics is extremely time consuming and requires expert knowledge of the different metaheuristics that are hybridized. In previous work, the effectiveness of automating the design of relay hybrid metaheuristics has been established. A genetic algorithm was used to determine the sequence of hybridized metaheuristics and the parameters of the metaheuristics in the hybrid. This study extends this idea by automating the design of each metaheuristic involved in the hybridization in addition to automating the design of the hybridization. A template is specified for each metaheuristic, defining the metaheuristic in terms of components. Manual design of metaheuristics usually involves determining the components of the metaheuristic. In this study, a genetic algorithm is employed to determine the components and parameters for each metaheuristic as well as the sequence of hybridized metaheuristics. The proposed genetic algorithm approach was evaluated by using it to automatically design hybrid metaheuristics for two problem domains, namely, the aircraft landing problem and the two-dimensional bin packing problem. The automatically designed hybrid metaheuristics were found to perform competitively to state-of-the-art hybridized metaheuristics for both problems. Future research will extend these ideas by looking at automating the derivation of metaheuristic algorithms without predefined structures specified by the templates.en_ZA
dc.description.departmentComputer Scienceen_ZA
dc.description.embargo2020-09-15
dc.description.librarianhj2019en_ZA
dc.description.sponsorshipThe National Research Foundation (NRF), South Africaen_ZA
dc.description.urihttp://www.elsevier.com/locate/eswaen_ZA
dc.identifier.citationHassan, A. & Pillay, N. 2019,'Hybrid metaheuristics : an automated approach', Expert Systems with Applications, vol. 130, pp. 132-144.en_ZA
dc.identifier.issn0957-4174 (print)
dc.identifier.issn1873-6793 (online)
dc.identifier.other10.1016/j.eswa.2019.04.027
dc.identifier.urihttp://hdl.handle.net/2263/71098
dc.language.isoenen_ZA
dc.publisherElsevieren_ZA
dc.rights© 2019 Elsevier Ltd. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in Expert Systems with Applications, vol. 130, pp. 132-144, 2019. doi : 10.1016/j.eswa.2019.04.027.en_ZA
dc.subjectHybrid metaheuristicen_ZA
dc.subjectMeta-genetic algorithmen_ZA
dc.subjectAutomated designen_ZA
dc.titleHybrid metaheuristics : an automated approachen_ZA
dc.typePostprint Articleen_ZA

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