Comparer des méthodes

	Optimisation bayésienne par essaims particulaires	Optimisation bayésienne
Domaine	Simulation	Optimisation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2003	1975 (foundational); 2012 (ML standard)
Auteur d'origine	Higashi, N., Iba, H. (extending Kennedy and Eberhart's PSO)	Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)
Type	Hybrid metaheuristic — Bayesian probabilistic swarm search	Sequential model-based black-box optimization
Source fondatrice	Higashi, N., Iba, H. (2003). Particle swarm optimization with Gaussian mutation. Proceedings of the 2003 IEEE Swarm Intelligence Symposium, Indianapolis, IN, USA, pp. 72-79.	Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25.
Alias	Bayesian PSO, BPSO, Probabilistic Swarm Optimization, Prior-guided PSO	Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO
Apparentées	6	2
Résumé	Bayesian Particle Swarm Optimization (Bayesian PSO) integrates Bayesian probabilistic reasoning into the standard particle swarm framework. Particles update their velocities and positions guided not only by personal and global best positions but also by a Bayesian posterior that encodes prior knowledge about the solution space, enabling more directed and statistically principled exploration of complex optimization landscapes.	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.