Wild Gibbon Optimization Algorithm

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.