Σύγκριση μεθόδων

	Αλγόριθμος Honey Badger	Βελτιστοποιητής Γκρίζου Λύκου
Πεδίο	Βελτιστοποίηση	Βελτιστοποίηση
Οικογένεια	Machine learning	Process / pipeline
Έτος προέλευσης	2023	2014
Δημιουργός	Fatma A. Hashim	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis
Τύπος	Nature-inspired metaheuristic algorithm	Swarm-intelligence metaheuristic
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	HBA	GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)
Συναφείς	5	5
Σύνοψη	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.