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	Optymalizacja Roju Cząstek z Podejściem Bayesowskim	Bayesowski algorytm genetyczny
Dziedzina	Symulacja	Symulacja
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	2003	1999
Twórca	Higashi, N., Iba, H. (extending Kennedy and Eberhart's PSO)	Pelikan, M., Goldberg, D. E., & Cantu-Paz, E.
Typ	Hybrid metaheuristic — Bayesian probabilistic swarm search	Evolutionary metaheuristic with Bayesian probabilistic model
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.	Pelikan, M., Goldberg, D. E., & Cantu-Paz, E. (1999). BOA: The Bayesian optimization algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-1999), pp. 525–532. Morgan Kaufmann.
Inne nazwy	Bayesian PSO, BPSO, Probabilistic Swarm Optimization, Prior-guided PSO	BGA, Bayesian-guided GA, Probabilistic GA, EDA-GA
Pokrewne	6	5
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.	A Bayesian Genetic Algorithm (BGA) replaces traditional crossover and mutation operators with a probabilistic Bayesian network learned from selected high-fitness individuals. At each generation the algorithm builds a graphical model of promising solution structure, then samples new offspring from that model, enabling the search to capture and exploit variable dependencies that standard GAs miss.