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

• Published in: Expert systems with applications Vol. 236; p. 121413
• Main Authors: Wang, Baiyi, Zhang, Zipeng, Siarry, Patrick, Liu, Xinhua, Królczyk, Grzegorz, Hua, Dezheng, Brumercik, Frantisek, Li, Z.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2024; Elsevier
• Subjects: African vultures optimization algorithm; Computer Science; Henon chaotic mapping theory; Nonlinear adaptive incremental inertial weight factor; Reverse learning competition strategy; Reverse learning competition strategy; Henon chaotic mapping theory; African vultures optimization algorithm; Nonlinear adaptive incremental inertial weight factor
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2023.121413

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.