融合多源信息提升约束优化探索效率
Constrained Bayesian Optimisation with Multiple Information Sources
- 构建多源信息联合框架，捕捉不同数据源间相关性
- 在辅助数据弱相关时仍能高效找到可行最优解
- 特别适合早期探索阶段，优于现有方法
未知约束下的贝叶斯优化在可行区域较小时尤为困难。现有方法通常仅依赖真实目标与约束评估，在此类场景下难以高效探索设计空间。然而，许多现实应用提供辅助数据源（如代理模型或简化模拟），可支持早期探索。尽管潜力巨大，这些辅助数据在约束贝叶斯优化中的整合仍少有研究。本文提出一种通用的多源框架，扩展了约束最大值熵搜索，能够捕捉多源间相关性，同时平衡评估成本与信息增益。在合成与物理基准测试中，该方法即使在辅助数据仅弱相关时，仍能高效识别可行且最优解，且在早期探索阶段表现显著优于现有方法。
原文摘要 · Abstract (English)
Bayesian Optimisation (BO) under unknown constraints is particularly challenging when feasible regions are small. In such settings, existing methods that typically rely solely on evaluations of the true objective and constraints struggle to efficiently explore the design space. However, many real-world applications offer auxiliary data sources (e.g. surrogate models or simplified simulations) that can support early exploration. Despite this potential, their integration into constrained BO remains largely unexplored. We propose a general multi-source framework that extends constrained Max-value Entropy Search, capturing inter-source correlation while balancing evaluation cost and information gain. Experiments on both synthetic and physics-based benchmarks show that our method efficiently identifies feasible and optimal solutions, even when auxiliary data are only weakly correlated. The proposed approach consistently outperforms existing methods, particularly in early-stage exploration.
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