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Open Access

ARTICLE

Wild Gibbon Optimization Algorithm

Jia Guo1,2,4,6, Jin Wang2, Ke Yan3, Qiankun Zuo1,2,4,*, Ruiheng Li1,2,4, Zhou He1,2,4, Dong Wang5, Yuji Sato6
1 Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, Wuhan, 430205, China
2 School of Information Engineering, Hubei University of Economics, Wuhan, 430205, China
3 China Construction Third Engineering Bureau Installation Engineering Co., Ltd., Wuhan, 430074, China
4 Hubei Internet Finance Information Engineering Technology Research Center, Hubei University of Economics, Wuhan, 430205, China
5 College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
6 Faculty of Computer and Information Sciences, Hosei University, Tokyo, 184-8584, Japan
* Corresponding Author: Qiankun Zuo. Email: email
(This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.051336

Received 02 March 2024; Accepted 07 June 2024; Published online 08 July 2024

Abstract

Complex optimization problems hold broad significance across numerous fields and applications. However, as the dimensionality of such problems increases, issues like the curse of dimensionality and local optima trapping also arise. To address these challenges, this paper proposes a novel Wild Gibbon Optimization Algorithm (WGOA) based on an analysis of wild gibbon population behavior. WGOA comprises two strategies: community search and community competition. The community search strategy facilitates information exchange between two gibbon families, generating multiple candidate solutions to enhance algorithm diversity. Meanwhile, the community competition strategy reselects leaders for the population after each iteration, thus enhancing algorithm precision. To assess the algorithm’s performance, CEC2017 and CEC2022 are chosen as test functions. In the CEC2017 test suite, WGOA secures first place in 10 functions. In the CEC2022 benchmark functions, WGOA obtained the first rank in 5 functions. The ultimate experimental findings demonstrate that the Wild Gibbon Optimization Algorithm outperforms others in tested functions. This underscores the strong robustness and stability of the gibbon algorithm in tackling complex single-objective optimization problems.

Keywords

Complex optimization; wild gibbon optimization algorithm; community search; community competition

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