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| 베이지안 입자 군집 최적화× | 확률적 입자 군집 최적화× | |
|---|---|---|
| 분야 | 시뮬레이션 | 시뮬레이션 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 2003 | 1995–2002 |
| 창시자≠ | Higashi, N., Iba, H. (extending Kennedy and Eberhart's PSO) | Kennedy, J. and Eberhart, R. (base PSO); stochastic extensions by Clerc, Kennedy and community |
| 유형≠ | Hybrid metaheuristic — Bayesian probabilistic swarm search | Metaheuristic optimization — stochastic swarm intelligence |
| 원전≠ | 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 ↗ | Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, Vol. 4, pp. 1942-1948. IEEE. DOI ↗ |
| 별칭 | Bayesian PSO, BPSO, Probabilistic Swarm Optimization, Prior-guided PSO | Stochastic PSO, SPSO, Randomized PSO, Probabilistic PSO |
| 관련≠ | 6 | 4 |
| 요약≠ | 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. |
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