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dc.contributor.authorAl-Dabbagh, R.D.en
dc.contributor.authorNeri, Ferranteen
dc.contributor.authorIdris, N.en
dc.contributor.authorBaba, M.S.en
dc.date.accessioned2018-03-20T08:42:09Z
dc.date.available2018-03-20T08:42:09Z
dc.date.issued2018-05-09
dc.identifier.citationAl-Dabbagh, R.D., Neri, F., Idris, N., Baba, M.S. (2018) Algorithm Design Issues in Adaptive Differential Evolution: Review and taxonomy. Swarm and Evolutionary Computation, 43, pp. 284-311en
dc.identifier.issn2210-6502
dc.identifier.urihttp://hdl.handle.net/2086/15528
dc.description.abstractThe performance of most metaheuristic algorithms depends on parameters whose settings essentially serve as a key function in determining the quality of the solution and the efficiency of the search. A trend that has emerged recently is to make the algorithm parameters automatically adapt to different problems during optimization, thereby liberating the user from the tedious and time-consuming task of manual setting. These fine-tuning techniques continue to be the object of ongoing research. Differential evolution (DE) is a simple yet powerful population-based metaheuristic. It has demonstrated good convergence, and its principles are easy to understand. DE is very sensitive to its parameter settings and mutation strategy; thus, this study aims to investigate these settings with the diverse versions of adaptive DE algorithms. This study has two main objectives: (1) to present an extension for the original taxonomy of evolutionary algorithms (EAs) parameter settings that has been overlooked by prior research and therefore minimize any confusion that might arise from the former taxonomy and (2) to investigate the various algorithmic design schemes that have been used in the different variants of adaptive DE and convey them in a new classification style. In other words, this study describes in depth the structural analysis and working principle that underlie the promising and recent work in this field, to analyze their advantages and disadvantages and to gain future insights that can further improve these algorithms. Finally, the interpretation of the literature and the comparative analysis of the results offer several guidelines for designing and implementing adaptive DE algorithms. The proposed design framework provides readers with the main steps required to integrate any proposed meta-algorithm into parameter and/or strategy adaptation schemes.en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectMetaheuristic algorithmsen
dc.subjectOptimization algorithmsen
dc.subjectEvolutionary Algorithmsen
dc.subjectGenetic Algorithmen
dc.subjectParameter Controlen
dc.subjectDifferential Evolutionen
dc.subjectAdaptive Differential Evolutionen
dc.titleAlgorithm Design Issues in Adaptive Differential Evolution: Review and taxonomyen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1016/j.swevo.2018.03.008
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NCen
dc.date.acceptance2018-03-18en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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