发表机构
Xidian University; Xi’an Jiaotong University（西安电子科技大学; 西安交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对实际工业设计中约束阈值难预先确定及现有方法不足，提出约束边界不可知贝叶斯优化（CBA-BO）框架，学习阈值到最优解的映射，可直接预测任意阈值配置的解，实验验证其有效性，还开发了推荐机制提升性能。
AI 中文摘要
在实际工业设计中，昂贵的约束优化问题常常涉及难以预先确定的约束阈值。工程师可能需要调整约束阈值以探索不同的可行性-性能权衡，这需要在广泛的阈值设置下找到解决方案。然而，现有的约束贝叶斯优化方法独立处理每个阈值配置，导致重复优化且无法利用连续变化阈值之间的共享关系。为应对这一挑战，我们提出了约束边界不可知贝叶斯优化（CBA-BO），这是一个基于学习的框架，它学习一个将阈值映射到最优解的参数化约束模型。一旦学习完成，CBA-BO无需额外优化就能直接预测任意未见阈值配置的解，通过一步贝叶斯优化细化可进一步提高解的质量。在基准和工程问题上的实验表明，CBA-BO学习到了可转移的阈值-解映射，能够对任意阈值查询进行高效预测和优化。我们还进一步开发了意图引导的约束边界推荐机制，以在满足用户指定的约束偏好的同时提高目标性能。
英文摘要
Expensive constrained optimization problems in real-world industry design often involve constraint thresholds that are difficult to determine in advance. Engineers may need to adjust constraint thresholds to explore different feasibility-performance trade-offs, requiring solutions under a wide range of threshold settings. However, existing constrained Bayesian optimization methods treat each threshold configuration independently, leading to repeated optimization and failing to exploit the shared relationship among continuously varying thresholds. To address this challenge, we propose constraint-bound agnostic Bayesian optimization (CBA-BO), a learning-based framework that learns a parametric constraint model mapping thresholds to optimal solutions. Once learned, CBA-BO directly predicts solutions for arbitrary unseen threshold configurations without additional optimization, with a one-step Bayesian optimization refinement further improving solution quality. Experiments on benchmark and engineering problems demonstrate that CBA-BO learns a transferable threshold-solution mapping, enabling efficient prediction and optimization for arbitrary threshold queries. An intent-guided constraint-bound recommendation mechanism is further developed to improve objective performance while satisfying user-specified constraint preferences.
