单目标采集函数与对冲策略的简单扩展用于多目标贝叶斯优化
Simple Extensions of Single-Objective Acquisition Functions and Hedge Strategies for Multi-Objective Bayesian Optimization
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中文总结 AI 辅助
本文提出通过超体积变换和对冲策略扩展单目标采集函数至多目标贝叶斯优化，简化流程，实验证明其性能匹配或超越复杂MOBO方法。
中文摘要 AI 辅助
多目标贝叶斯优化（MOBO）通常通过专门设计的采集函数或标量化方案来处理，这些方法旨在明确考虑非偏好目标之间的权衡。在这项工作中，我们表明这种复杂性可能是不必要的。我们提出了一个框架，通过基于超体积的变换将标准单目标采集函数直接扩展到多目标设置。我们进一步将对冲策略（通常仅在单目标优化中使用）扩展到多目标领域。我们的方法需要对现有贝叶斯优化流程进行最小修改，并避免了对专门多目标公式的需求。我们展示了如何以原则性的方式将广泛使用的单目标采集函数和对冲策略适应于处理多个目标，同时保持其直观解释和计算效率。在实验上，我们在各种合成和真实世界的多目标基准上评估了所提出的方法。尽管它们简单，我们的扩展在优化性能和样本效率方面始终匹配或超越更复杂的现有MOBO方法。这些结果表明，通过重用并仔细扩展成熟的单目标采集策略，可以实现有效的多目标贝叶斯优化，为现有方法提供更简单且更灵活的替代方案。
英文摘要
Multi-objective Bayesian optimization (MOBO) is commonly approached through specialized acquisition functions or scalarization schemes designed to explicitly account for trade-offs among non-preferential objectives. In this work, we show that such complexity might be unnecessary. We propose a framework that extends standard single-objective acquisition functions directly to the multi-objective setting through a hypervolume-based transformation. We further extend hedge strategies for acquisition functions, which are typically used only in single-objective optimization, to the multi-objective regime. Our approach requires minimal modification to existing Bayesian optimization pipelines and avoids the need for bespoke multi-objective formulations. We demonstrate how a broad class of commonly used single-objective acquisition functions and hedge strategies can be adapted in a principled manner to handle multiple objectives, while preserving their intuitive interpretation and computational efficiency. Empirically, we evaluate the proposed methods across a range of synthetic and real-world multi-objective benchmarks. Despite their simplicity, our extensions consistently match or outperform more complex state-of-the-art MOBO methods in terms of optimization performance and sample efficiency. These results suggest that effective multi-objective Bayesian optimization can be achieved by reusing and carefully extending well-established single-objective acquisition strategies, offering a simpler and more flexible alternative to existing approaches.
