DA Strategy Implementation toward Optimization Problems: A Systematic Literature Review

Real-world problems demand an optimal solution within an acceptable time. Normally the problems are compound in nature, often not polynomials (NP-Problem) based on complexity theory. One of the approaches to solving real-world problems is optimization. The latest development of optimization known as...

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Bibliographic Details
Published in:14th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2024
Main Author: Hamami M.G.M.; Ismail Z.H.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198903802&doi=10.1109%2fISCAIE61308.2024.10576343&partnerID=40&md5=e6641decbe3e4bda13e6817b7b0792e3
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Summary:Real-world problems demand an optimal solution within an acceptable time. Normally the problems are compound in nature, often not polynomials (NP-Problem) based on complexity theory. One of the approaches to solving real-world problems is optimization. The latest development of optimization known as intelligent optimization (IO) or specifically meta-heuristic optimization has the characteristics and capability to tackle these real-world problems. In 2016 the Dragonfly Algorithm (DA) strategy was developed for meta-heuristic optimization purposes. Since then, the DA has gathered the thrust of the research community by implementing it across multiple research domains and applications. This systematic literature review (SLR) article has been introduced with the intent to dissect and analyze the DA strategy implementation toward optimization problems. Guided with the PRISMA method, a total of 62 related research studies have been identified and eligible for further analysis. The analysis has been done with the consideration of three main elements which are the DA variant, application field, and benefited research domain. Based on the obtained SLR analyzed data, several discussions, opinions, and recommendations have been thoughtfully presented. © 2024 IEEE.
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DOI:10.1109/ISCAIE61308.2024.10576343