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.

翻译：为缓解非洲秃鹫优化算法（AVOA）的主要缺陷，提出了一种结合Henon混沌映射理论与反向学习竞争策略的非线性非洲秃鹫优化算法（HWEAVOA）。首先，引入Henon混沌映射理论与精英种群策略以提升秃鹫初始种群的随机性与多样性；其次，在位置更新阶段引入非线性自适应增量惯性权重因子，合理平衡探索与开发能力，避免个体陷入局部最优；此外，设计反向学习竞争策略以扩展最优解的发现域，增强跳出局部最优解的能力。采用HWEAVOA及其他先进对比算法求解经典及CEC2022测试函数。实验结果表明，与其他算法相比，HWEAVOA的收敛曲线下降更快且线条更平滑。在所有测试函数中，所提HWEAVOA均排名第一，在收敛速度、优化能力及解稳定性方面优于对比算法。同时，HWEAVOA的算法复杂度达到一般水平，其整体性能在群智能算法中具备竞争力。