Methoden vergleichen

	Partikelschwarmoptimierung (PSO)	Bayesian Optimization
Fachgebiet	Optimierung	Optimierung
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1995	1975 (foundational); 2012 (ML standard)
Urheber	—	Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)
Typ	Population-based metaheuristic / swarm intelligence	Sequential model-based black-box optimization
Wegweisende Quelle	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.	Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25.
Aliasnamen	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)	Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO
Verwandt	6	2
Zusammenfassung	Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization 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.