Process / pipelineSimulation / optimization

Bayesian Particle Swarm Optimization — Probabilistic Prior-Guided Swarm Search

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.

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Sources

  1. 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. DOI: 10.1109/SIS.2003.1202250
  2. Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 — International Conference on Neural Networks, Perth, WA, Australia, vol. 4, pp. 1942-1948. DOI: 10.1109/ICNN.1995.488968

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ScholarGateBayesian Particle Swarm Optimization (Bayesian Particle Swarm Optimization — Probabilistic prior-guided swarm search). Retrieved 2026-06-04 from https://scholargate.app/en/simulation/bayesian-particle-swarm-optimization