Process / pipelineSimulation / optimization
贝叶斯模拟退火 — 带有贝叶斯先验的全局优化
贝叶斯模拟退火 (Bayesian Simulated Annealing, BSA) 将关于目标景观的贝叶斯先验知识整合到模拟退火搜索过程中。通过将有希望区域的信念编码为先验分布，并在搜索过程中更新它们，BSA 将计算资源集中在解空间的概率高区域，从而加速收敛并提高解的质量，优于无先验信息的模拟退火。
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来源
- Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680. DOI: 10.1126/science.220.4598.671 ↗
- Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6), 721–741. DOI: 10.1109/TPAMI.1984.4767596 ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Simulated Annealing — Probabilistic global optimization with Bayesian priors on the energy landscape. ScholarGate. https://scholargate.app/zh/simulation/bayesian-simulated-annealing
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