Whale optimization algorithm: a systematic review of contemporary applications, modifications and developments
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REVIEW
Whale optimization algorithm: a systematic review of contemporary applications, modifications and developments Nadim Rana1,2 • Muhammad Shafie Abd Latiff1 • Shafi’i Muhammad Abdulhamid3 • Haruna Chiroma4 Received: 5 July 2018 / Accepted: 14 March 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Whale optimization algorithm (WOA) is a recently developed swarm-based meta-heuristic algorithm that is based on the bubble-net hunting maneuver technique—of humpback whales—for solving the complex optimization problems. It has been widely accepted swarm intelligence technique in various engineering fields due to its simple structure, less required operator, fast convergence speed and better balancing capability between exploration and exploitation phases. Owing to its optimal performance and efficiency, the applications of the algorithm have extensively been utilized in multidisciplinary fields in the recent past. This paper investigates further into WOA of its applications, modifications, and hybridizations across various fields of engineering. The description of the strengths, weaknesses and opportunities to support future research are also explored. The Systematic Literature Review is opted as a method to disseminate the findings and gap from the existing literature. The authors select eighty-two (82) articles as a primary studies out of nine hundred and thirty-nine (939) articles between 2016 and 2020. As per our result, WOA-based techniques are applied in 5 fields and 17 subfields of various engineering domains. 61% work has been found on modification, 27% on hybridization and 12% on multiobjective variants of WOA techniques. The growing research trend on WOA is expected to continue into the future. The review presented in the paper has the potential to motivate expert researchers to propose more novel WOA-based algorithms, and it can serve as an initial reading material for a novice researcher. Keywords Whale optimization algorithm Meta-heuristic Swarm based Bubble-net hunting
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00521-020-04849-z) contains supplementary material, which is available to authorized users. & Nadim Rana [email protected]; [email protected] Muhammad Shafie Abd Latiff [email protected] Shafi’i Muhammad Abdulhamid [email protected] Haruna Chiroma [email protected] 1
Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia
2
College of Comuter Science and Information Technology, Jazan University, Jazan, Kingdom of Saudi Arabia
3
Department of Cyber Security Science, Federal University of Technology, Minna, Niger State, Nigeria
4
Future Technology Research Center, National Yunlin University of Science and Technology, Yunlin, Taiwan
1 Introduction Nature-inspired meta-heuristic algorithms belong to the realm of computational intelligence (CI). Biology-based CI (BbCI), Physics-based CI (PbCI), Chemistry-based CI (Cb
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