Methoden vergleichen

	Grey Wolf Optimizer	Bayesian Optimization
Fachgebiet	Optimierung	Optimierung
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	2014	1975 (foundational); 2012 (ML standard)
Urheber	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis	Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)
Typ	Swarm-intelligence metaheuristic	Sequential model-based black-box optimization
Wegweisende Quelle	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.
Aliasnamen	GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)	Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO
Verwandt	5	2
Zusammenfassung	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.