Сравнение методов

	Байесовская оптимизация роем частиц	Оптимизация роем частиц (PSO)
Область	Имитационное моделирование	Оптимизация
Семейство	Process / pipeline	Process / pipeline
Год появления	2003	1995
Автор метода	Higashi, N., Iba, H. (extending Kennedy and Eberhart's PSO)	—
Тип	Hybrid metaheuristic — Bayesian probabilistic swarm search	Population-based metaheuristic / swarm intelligence
Основополагающий источник	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. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Другие названия	Bayesian PSO, BPSO, Probabilistic Swarm Optimization, Prior-guided PSO	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Связанные	6	6
Сводка	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.	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.