مقایسهٔ روش‌ها

	بهینه‌ساز عقاب طلایی (Aquila Optimizer)	بهینه‌سازی شاهین هریس
حوزه	بهینه‌سازی	بهینه‌سازی
خانواده	Machine learning	Machine learning
سال پیدایش	2021	2019
پدیدآور	Laith Abualigah	Ali Asghar Heidari
نوع	Nature-inspired metaheuristic algorithm	Nature-inspired metaheuristic algorithm
منبع بنیادین	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.	Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872.
نام‌های دیگر	AO	HHO
مرتبط	3	4
خلاصه	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.	Harris Hawks Optimization (HHO) is a metaheuristic algorithm introduced by Heidari et al. in 2019, inspired by the hunting strategies of Harris's hawks. The algorithm models the cooperative hunting behavior and escape strategies of these raptors to solve complex optimization problems. HHO balances exploration through perching and exploitation through dynamic pursuit, making it effective for multimodal and high-dimensional optimization.