Staff View: Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy

Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy

The 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 differen...

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Main Authors:	Al-Dabbagh, Rawaa Dawoud, Neri, Ferrante, Idris, Norisma, Baba, Mohd Sapiyan
Format:	Article
Published:	Elsevier 2018
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.um.edu.my/22583/ https://doi.org/10.1016/j.swevo.2018.03.008
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id	my.um.eprints.22583
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spelling	my.um.eprints.225832019-09-26T06:34:04Z http://eprints.um.edu.my/22583/ Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy Al-Dabbagh, Rawaa Dawoud Neri, Ferrante Idris, Norisma Baba, Mohd Sapiyan QA75 Electronic computers. Computer science The 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 algorithmic schemes 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. Elsevier 2018 Article PeerReviewed Al-Dabbagh, Rawaa Dawoud and Neri, Ferrante and Idris, Norisma and Baba, Mohd Sapiyan (2018) Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy. Swarm and Evolutionary Computation, 43. pp. 284-311. ISSN 2210-6502 https://doi.org/10.1016/j.swevo.2018.03.008 doi:10.1016/j.swevo.2018.03.008
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Al-Dabbagh, Rawaa Dawoud Neri, Ferrante Idris, Norisma Baba, Mohd Sapiyan Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy
description	The 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 algorithmic schemes 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.
format	Article
author	Al-Dabbagh, Rawaa Dawoud Neri, Ferrante Idris, Norisma Baba, Mohd Sapiyan
author_facet	Al-Dabbagh, Rawaa Dawoud Neri, Ferrante Idris, Norisma Baba, Mohd Sapiyan
author_sort	Al-Dabbagh, Rawaa Dawoud
title	Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy
title_short	Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy
title_full	Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy
title_fullStr	Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy
title_full_unstemmed	Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy
title_sort	algorithmic design issues in adaptive differential evolution schemes: review and taxonomy
publisher	Elsevier
publishDate	2018
url	http://eprints.um.edu.my/22583/ https://doi.org/10.1016/j.swevo.2018.03.008
_version_	1646210282876305408
score	13.211869

Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy

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