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A nonlinear African vulture optimization algorithm combining Henon chaotic mapping theory and reverse learning competition strategy 作者： Baiyi Wang 1 Zipeng Zhang 1 Patrick Siarry 2 Xinhua Liu 1 Grzegorz Królczyk 3 Dezheng Hua 1 Frantisek Brumercik 4 Zhixiong Li 3 作者单位： 1. School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, China 2. University Paris-Est Créteil Val de Marne, 61 Av. du General de Gaulle, Creteil 94010, France 3. Department of Manufacturing Engineering and Automation Products, Opole University of Technology, Opole 45-758, Poland 4. Department of Design and Machine Elements, Faculty of Mechanical Engineering, University of Zilina, Univerzitna 1, Zilina 010 26, Slovakia 通讯作者： Xinhua Liu Email:[email protected] 提交时间： 2024-03-28 18:49:16 摘要: As a new intelligent optimization algorithm, the African vultures optimization algorithm (AVOA) has been widely used in various fields today. However, when solving complex multimodal problems, the AVOA still has some shortcomings, such as low searching accuracy, deficiency on the search capability and tendency to fall into local optimum. In order to alleviate the main shortcomings of the AVOA, a nonlinear African vulture optimization algorithm combining Henon chaotic mapping theory and reverse learning competition strategy (HWEAVOA) is proposed. Firstly, the Henon chaotic mapping theory and elite population strategy are proposed to improve the randomness and diversity of the vulture’s initial population; Furthermore, the nonlinear adaptive incremental inertial weight factor is introduced in the location update phase to rationally balance the exploration and exploitation abilities, and avoid individual falling into a local optimum; The reverse learning competition strategy is designed to expand the discovery fields for the optimal solution and strengthen the ability to jump out of the local optimal solution. HWEAVOA and other advanced comparison algorithms are used to solve classical and CEC2022 test functions. Compared with other algorithms, the convergence curves of the HWEAVOA drop faster and the line bodies are smoother. These experimental results show the proposed HWEAVOA is ranked first in all test functions, which is superior to the comparison algorithms in convergence speed, optimization ability, and solution stability. Meanwhile, HWEAVOA has reached the general level in the algorithm complexity, and its overall performance is competitive in the swarm intelligence algorithms. African vultures optimization algorithm Henon chaotic mapping theory Nonlinear adaptive incremental inertial weight factor Reverse learning competition strategy 来自： Zhixiong Li 分类： 工程与技术科学 >> 工程通用技术 投稿状态： 已被期刊接收 引用： ChinaXiv:202403.00412 (或此版本 ChinaXiv:202403.00412V1 ) DOI:10.12074/202403.00412V1 CSTR:32003.36.ChinaXiv.202403.00412.V1 推荐引用方式： Baiyi Wang ,Zipeng Zhang,Patrick Siarry,Xinhua Liu,Grzegorz Królczyk,Dezheng Hua,Frantisek Brumercik,Zhixiong Li.(2024).A nonlinear African vulture optimization algorithm combining Henon chaotic mapping theory and reverse learning competition strategy.中国科学院科技论文预发布平台.[ChinaXiv:202403.00412] 版本历史 [V1] 2024-03-28 18:49:16 ChinaXiv:202403.00412V1 下载全文