A nonlinear African vulture optimization algorithm combining Henon chaotic mapping theory and reverse learning competition strategy

分类： 工程与技术科学 >> 工程通用技术 提交时间： 2024-03-28

Baiyi Wang Zipeng Zhang Patrick Siarry Xinhua Liu Grzegorz Królczyk Dezheng Hua Frantisek Brumercik Zhixiong Li

摘要： As a new intelligentoptimization algorithm, the African vulturesoptimization 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 thelocal 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 thealgorithm complexity, and its overall performance is competitive in theswarm intelligence algorithms.

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