BOCoDe：面向工程设计的贝叶斯优化基准测试
BOCoDe: Engineering-Centered Benchmarking for Bayesian Optimization
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中文总结 AI 辅助
该研究针对贝叶斯优化的基准测试与工程设计场景不匹配的问题，推出开源BOCoDe基准集，含307个黑盒优化问题，评估31种算法，发现标准基准排名无法迁移至工程任务，为BO方法开发提供支撑。
中文摘要 AI 辅助
贝叶斯优化（Bayesian Optimization, BO）是一种基于代理模型的样本高效黑盒优化（Black-Box Optimization, BBO）方法，但其评估仍以合成函数和超参数优化（Hyperparameter Optimization, HPO）任务为主，这类任务通常是低维、单目标的。工程设计则呈现出截然不同的场景：问题基于物理规律，往往是高维的，受成本、可制造性等要求约束，还可能涉及多目标或混合变量。为缩小这一基准测试差距，我们推出BOCoDe——一个开源、原生PyTorch的基准测试集，包含307个BBO问题，其中包括159个工程设计任务以及广泛使用的合成函数和HPO基准。每个问题都有引用来源和机器可读元数据，支持程序化发现，包括基于大语言模型（LLM）的智能体，所有任务均通过与开源BO库兼容的统一API提供。我们在五个问题类别中评估了31种BO和进化算法，涵盖单目标与多目标优化、带约束与无约束设置、混合变量搜索空间。对问题结构的分析显示，工程任务独特地涵盖了合成和HPO基准很少涉及的带约束与多目标设置，而表格基础模型的嵌入能最清晰地将它们与HPO任务区分开。算法排名在不同领域差异显著；在多个问题类别中，标准基准上得到的排名无法迁移到工程任务。BOCoDe为开发和评估更贴合工程设计需求的BO方法奠定了可复现且可扩展的基础。代码与数据可在指定URL获取。
英文摘要
Bayesian optimization (BO) is a sample-efficient, surrogate-based approach to black-box optimization (BBO), but its evaluation remains dominated by synthetic functions and hyperparameter optimization (HPO) tasks that are typically low-dimensional and single-objective. Engineering design poses a substantially different regime: problems are physics-based, often high-dimensional, constrained by requirements such as cost and manufacturability, and may involve multiple objectives or mixed variables. To close this benchmarking gap, we introduce BOCoDe, an open-source, PyTorch-native benchmark comprising 307 BBO problems, including 159 engineering design tasks and widely used synthetic and HPO benchmarks. Each problem includes cited provenance and machine-readable metadata that supports programmatic discovery, including by LLM-based agents, and all tasks are exposed through a unified API compatible with open-source BO libraries. We evaluate 31 BO and evolutionary algorithms across five problem classes spanning single- and multi-objective optimization, constrained and unconstrained settings, and mixed-variable search spaces. Analyses of problem structure show that engineering tasks uniquely span constrained and multi-objective settings that synthetic and HPO suites rarely cover, while embeddings from a tabular foundation model separate them most clearly from HPO tasks. Algorithm rankings also vary substantially across domains; in several problem classes, rankings obtained on standard benchmarks do not transfer to engineering tasks. BOCoDe establishes a reproducible and extensible foundation for developing and evaluating BO methods that better reflect the demands of engineering design. Code & data can be found at https://github.com/rosenyu304/BOCoDe
