  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.
