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
CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1203-1233, 2024, DOI:10.32604/cmc.2024.051336
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, More >
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