Сравнение методов

	Алгоритм оптимизации африканских стервятников	Оптимизатор "Орел" (Aquila Optimizer, AO)
Область	Оптимизация	Оптимизация
Семейство	Machine learning	Machine learning
Год появления	2020	2021
Автор метода	Hossein Moghdani	Laith Abualigah
Тип	Nature-inspired metaheuristic algorithm	Nature-inspired metaheuristic algorithm
Основополагающий источник	Moghdani, H., & Salimifard, K. (2020). Volleyball player optimizer and African vultures optimization algorithms for solving global optimization problems. Applied Soft Computing, 97, 106794.	Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Aquila optimizer: A novel meta-heuristic optimization algorithm. Computers and Industrial Engineering, 157, 107250.
Другие названия	AVOA	AO
Связанные	4	3
Сводка	The African Vultures Optimization Algorithm (AVOA) is a metaheuristic algorithm introduced by Moghdani and Salimifard in 2020, inspired by the search and scavenging behavior of African vultures. Vultures employ sophisticated collaborative strategies to locate carrion across vast distances, using thermal air currents and group dynamics to navigate efficiently. AVOA translates these collective hunting behaviors into an effective optimization framework.	The Aquila Optimizer (AO) is a nature-inspired metaheuristic algorithm presented by Abualigah et al. in 2021, modeled after the hunting behavior and sensory abilities of golden eagles (aquila chrysaetos). The algorithm captures the exploration and exploitation phases of eagle hunting, including high-altitude soaring, exploration with high-precision vision, and rapid diving attacks. AO is designed to solve both constrained and unconstrained optimization problems.