Optimizing black-box functions in high-dimensional search spaces has been known to be challenging for traditional Bayesian Optimization (BO). In this paper, we introduce HiBO, a novel hierarchical algorithm integrating global-level search space partitioning information into the acquisition strategy of a local BO-based optimizer. HiBO employs a search-tree-based global-level navigator to adaptively split the search space into partitions with different sampling potential. The local optimizer then utilizes this global-level information to guide its acquisition strategy towards most promising regions within the search space. A comprehensive set of evaluations demonstrates that HiBO outperforms state-of-the-art methods in high-dimensional synthetic benchmarks and presents significant practical effectiveness in the real-world task of tuning configurations of database management systems (DBMSs).

翻译：在高维搜索空间中优化黑箱函数对于传统贝叶斯优化（BO）而言一直具有挑战性。本文提出HiBO，一种新颖的层次化算法，该算法将全局层面的搜索空间划分信息整合到基于局部BO优化器的采集策略中。HiBO采用基于搜索树的全局导航器，将搜索空间自适应地划分为具有不同采样潜力的分区。随后，局部优化器利用这一全局层面信息引导其采集策略朝向搜索空间内最具潜力的区域。全面的实验评估表明，HiBO在高维合成基准测试中优于现有先进方法，并在数据库管理系统（DBMS）配置调优这一实际任务中展现出显著的实用效能。