مقایسهٔ روش‌ها

	بهینه‌سازی شاهین هریس	بهینه‌ساز عقاب طلایی (Aquila Optimizer)
حوزه	بهینه‌سازی	بهینه‌سازی
خانواده	Machine learning	Machine learning
سال پیدایش	2019	2021
پدیدآور	Ali Asghar Heidari	Laith Abualigah
نوع	Nature-inspired metaheuristic algorithm	Nature-inspired metaheuristic algorithm
منبع بنیادین	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.	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.
نام‌های دیگر	HHO	AO
مرتبط	4	3
خلاصه	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.	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.