Recent advances of whale optimization algorithm, its versions and applications
Swarm intelligence (SI) is an approach inspired by natural phenomena that have been implemented in the optimization field. This field has rapidly increased very fast recently. The main idea behind the SI is to transfer the interactions between living organisms into a mathematical model that can find...
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| Main Authors: | , , , , , , , |
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| Format: | Book chapter |
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Elsevier
2024
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| Summary: | Swarm intelligence (SI) is an approach inspired by natural phenomena that have been implemented in the optimization field. This field has rapidly increased very fast recently. The main idea behind the SI is to transfer the interactions between living organisms into a mathematical model that can find the optimal solution for real-world problems based on biological behavior such as ants, birds, and fish. One of the SI algorithms is called the whale optimization algorithm (WOA). The WOA is a robust optimization algorithm that mimics the social behavior of humpback whales in nature. The WOA was proposed by Mirjalili in 2016 and its success implement in different real-world problems. This chapter reviewed and analyzed the recent works published using WOA from 2021 to 2022. The WOA has very impressive characteristics such as its easy-to-use, simple in concepts, flexibility and adaptability, consistency, sound, and completeness. Initially, the growth of the recent solid works published in Scopus-indexed articles is summarized in terms of the number of WOA-based top institutions, top publishers, and top countries. Then, the different versions of WOA are highlighted to be in line with the complex nature of optimization problems such as binary, modified, multiobjective, and hybridized of the WOA. The successful applications of WOA are summarized. The open-source codes of the WOA code are given to build a wealthy WOA review. Finally, the WOA review is concluded. The reader of this review will determine the best domains and applications used by WOA and can justify their WOA-related contributions. � 2024 Elsevier Inc. All rights reserved. |
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