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	Optymalizacja Roju Cząstek z Podejściem Bayesowskim	Stochastyczne Optymalizowanie Roju Cząstek
Dziedzina	Symulacja	Symulacja
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	2003	1995–2002
Twórca	Higashi, N., Iba, H. (extending Kennedy and Eberhart's PSO)	Kennedy, J. and Eberhart, R. (base PSO); stochastic extensions by Clerc, Kennedy and community
Typ	Hybrid metaheuristic — Bayesian probabilistic swarm search	Metaheuristic optimization — stochastic swarm intelligence
Źródło pierwotne	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.	Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, Vol. 4, pp. 1942-1948. IEEE.
Inne nazwy	Bayesian PSO, BPSO, Probabilistic Swarm Optimization, Prior-guided PSO	Stochastic PSO, SPSO, Randomized PSO, Probabilistic PSO
Pokrewne	6	4
Podsumowanie	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.	Stochastic Particle Swarm Optimization (Stochastic PSO) is a swarm-intelligence metaheuristic that extends the standard PSO framework by incorporating explicit stochastic elements — random inertia weights, probabilistic velocity resets, or noise injections — to escape local optima and maintain population diversity throughout the search. It is widely applied to continuous, mixed, and noisy optimization problems in engineering, operations research, and simulation-based design.