There has been a surge in interest in data-driven experimental design with applications to chemical engineering and drug manufacturing. Bayesian optimization (BO) has proven to be adaptable to such cases, since we can model the reactions of interest as expensive black-box functions. Sometimes, the cost of this black-box functions can be separated into two parts: (a) the cost of the experiment itself, and (b) the cost of changing the input parameters. In this short paper, we extend the SnAKe algorithm to deal with both types of costs simultaneously. We further propose extensions to the case of a maximum allowable input change, as well as to the multi-objective setting.

翻译：近年来，数据驱动实验设计在化学工程和药物制造领域引起广泛关注。贝叶斯优化（BO）已被证明能适应此类场景，因为我们可以将感兴趣的反应建模为昂贵的黑箱函数。有时，黑箱函数的代价可分为两部分：（a）实验本身的代价，以及（b）改变输入参数的代价。本短文对SnAKe算法进行扩展，使其能够同时处理这两类代价。我们进一步提出了针对最大允许输入变化约束场景的扩展方法，以及面向多目标优化场景的扩展方案。