השוואת שיטות

	אופטימיזציית נצי חאריס	ממטב-טבע אלגוריתם האופטימיזציה אקווילה (Aquila Optimizer - AO)
תחום	אופטימיזציה	אופטימיזציה
משפחה	Machine learning	Machine learning
שנת המקור	2019	2021
הוגה השיטה	Ali Asghar Heidari	Laith Abualigah
סוג	Nature-inspired metaheuristic algorithm	Nature-inspired metaheuristic algorithm
מקור מכונן	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.
כינויים	HHO	AO
קשורות	4	3
תקציר	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.