Krahasoni metodat

	Optimizuesi ujku gri	Optimizimi Bayesiano
Fusha	Optimizimi	Optimizimi
Familja	Process / pipeline	Process / pipeline
Viti i origjinës	2014	1975 (foundational); 2012 (ML standard)
Krijuesi	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis	Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)
Lloji	Swarm-intelligence metaheuristic	Sequential model-based black-box optimization
Burimi themelues	Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.	Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25.
Emërtime të tjera	GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)	Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO
Të lidhura	5	2
Përmbledhja	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.	Bayesian Optimization is a sequential, model-based strategy for finding the optimum of expensive black-box functions with as few evaluations as possible. Rooted in the work of Mockus (1975) and brought to mainstream machine-learning practice by Snoek, Larochelle, and Adams (2012), it fits a probabilistic surrogate model — typically a Gaussian Process — to past observations and uses an acquisition function to decide where to probe next, balancing exploration of unknown regions with exploitation of promising ones.