On the exploration and exploitation in popular swarm-based metaheuristic algorithms

Hussain, Kashif and Mohd Salleh, Mohd Najib and Cheng, Shi and Shi, Yuhui (2018) On the exploration and exploitation in popular swarm-based metaheuristic algorithms. Neural Computing and Applications, 13 (3). pp. 7665-7683. ISSN 0941-0643

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Abstract

It is obvious from wider spectrum of successful applications that metaheuristic algorithms are potential solutions to hard optimization problems. Among such algorithms are swarm-based methods like particle swarm optimization and ant colony optimization which are increasingly attracting new researchers. Despite popularity, the core questions on performance issues are still partially answered due to limited insightful analyses. Mere investigation and comparison of end results may not reveal the reasons behind poor or better performance. This study, therefore, performed in-depth empirical analysis by quantitatively analyzing exploration and exploitation of five swarm-based metaheuristic algorithms. The analysis unearthed explanations the way algorithms performed on numerical problems as well as on real-world application of classification using adaptive neuro-fuzzy inference system (ANFIS) trained by selected metaheuristics. The outcome of empirical study suggested that coherence and consistency in the swarm individuals throughout iterations is the key to success in swarmbased metaheuristic algorithms. The analytical approach adopted in this study may be employed to perform componentwise diversity analysis so that the contribution of each component on performance may be determined for devising efficient search strategies.

Item Type:	Article
Uncontrolled Keywords:	Swarm intelligence; Metaheuristic; Population diversity; Exploration and exploitation; Optimization
Subjects:	Q Science > Q Science (General)
Divisions:	Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User:	UiTM Student Praktikal
Date Deposited:	17 Nov 2021 07:38
Last Modified:	17 Nov 2021 07:38
URI:	http://eprints.uthm.edu.my/id/eprint/3465

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