Sammenlign metoder

	Whale Optimization Algorithm (WOA)	Bayesiansk optimering
Fagområde	Optimering	Optimering
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	2016	1975 (foundational); 2012 (ML standard)
Ophavsperson	Seyedali Mirjalili & Andrew Lewis	Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)
Type	Swarm-based metaheuristic	Sequential model-based black-box optimization
Oprindelig kilde	Mirjalili, S. & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51-67.	Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25.
Aliasser	WOA, Balina Optimizasyon Algoritması (WOA), bubble-net attacking method	Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO
Relaterede	5	2
Resumé	The Whale Optimization Algorithm (WOA) is a swarm-based metaheuristic introduced by Mirjalili and Lewis in 2016. It models the bubble-net hunting strategy of humpback whales, in which a group of whales spirals around prey while gradually tightening the encirclement. The algorithm balances global exploration and local exploitation through a small set of parameters and has become widely used in continuous engineering optimisation problems.	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.