เปรียบเทียบวิธี

	อัลกอริทึมผึ้งหลวง	Grey Wolf Optimizer
สาขาวิชา	การหาค่าเหมาะที่สุด	การหาค่าเหมาะที่สุด
ตระกูล	Machine learning	Process / pipeline
ปีกำเนิด	2023	2014
ผู้ริเริ่ม	Fatma A. Hashim	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis
ประเภท	Nature-inspired metaheuristic algorithm	Swarm-intelligence metaheuristic
แหล่งต้นตำรับ	Hashim, F. A., Hussain, K., & Houssein, E. H. (2023). Honey badger algorithm: A new meta-heuristic optimization algorithm. Neural Computing and Applications, 35(17), 12265-12287.	Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.
ชื่อเรียกอื่น	HBA	GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)
ที่เกี่ยวข้อง	5	5
สรุป	The Honey Badger Algorithm (HBA) is a nature-inspired metaheuristic optimization algorithm presented by Hashim et al. in 2023, modeled on the hunting behavior and intelligent strategies of honey badgers (Mellivora capensis). Honey badgers are known for their remarkable problem-solving abilities, fearlessness, and persistent pursuit of prey and food sources despite significant obstacles. HBA captures these behavioral traits to create an effective optimization framework.	The Grey Wolf Optimizer (GWO) is a swarm-intelligence metaheuristic introduced by Mirjalili, Mirjalili, and Lewis in 2014 that models the social hierarchy and cooperative hunting behaviour of grey wolves. A population of candidate solutions is divided into four leadership ranks — alpha, beta, delta, and omega — and the three best solutions at each iteration guide the entire swarm toward increasingly better regions of the search space.