Comparar métodos

	Otimização por Enxame de Partículas Bayesiano	Otimização por Enxame de Partículas (PSO)
Área	Simulação	Otimização
Família	Process / pipeline	Process / pipeline
Ano de origem	2003	1995
Autor original	Higashi, N., Iba, H. (extending Kennedy and Eberhart's PSO)	—
Tipo	Hybrid metaheuristic — Bayesian probabilistic swarm search	Population-based metaheuristic / swarm intelligence
Fonte seminal	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.
Outros nomes	Bayesian PSO, BPSO, Probabilistic Swarm Optimization, Prior-guided PSO	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Relacionados	6	6
Resumo	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.