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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| Format: | Article |
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Elsevier
2018
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| _version_ | 1796961658006929408 |
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| 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 |
| collection | UM |
| 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. |
| first_indexed | 2024-03-06T05:57:24Z |
| format | Article |
| id | um.eprints-22583 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:57:24Z |
| publishDate | 2018 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | 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, DOI https://doi.org/10.1016/j.swevo.2018.03.008 <https://doi.org/10.1016/j.swevo.2018.03.008>. https://doi.org/10.1016/j.swevo.2018.03.008 doi:10.1016/j.swevo.2018.03.008 |
| 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 |
| title | 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_short | Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy |
| title_sort | algorithmic design issues in adaptive differential evolution schemes review and taxonomy |
| topic | QA75 Electronic computers. Computer science |
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