Process / pipelineSimulation / optimization
贝叶斯多目标优化——基于代理模型的帕累托前沿搜索与不确定性量化
贝叶斯多目标优化（BMOO/MOBO）使用高斯过程代理模型来近似多个昂贵的目标函数，并以最少的实际评估次数引导搜索趋向帕累托前沿。通过量化每个候选点处的预测不确定性，它在探索未知区域与利用有前景的解之间取得平衡，当每个函数评估在计算或实验上成本高昂时，它尤其强大。
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来源
- Svenson, J., Santner, T. (2016). Multiobjective optimization of expensive-to-evaluate deterministic computer simulator models. Computational Statistics & Data Analysis, 94, 250-264. DOI: 10.1016/j.csda.2015.08.011 ↗
- Emmerich, M., Giannakoglou, K., Naujoks, B. (2006). Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels. IEEE Transactions on Evolutionary Computation, 10(4), 421-439. DOI: 10.1109/TEVC.2005.859463 ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Multi-Objective Optimization (BMOO) — Surrogate-assisted Pareto frontier exploration under uncertainty. ScholarGate. https://scholargate.app/zh/simulation/bayesian-multi-objective-optimization
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