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.