Bayesian optimization (BO) is one of the most effective methods for closed-loop experimental design and black-box optimization. However, a key limitation of BO is that it is an inherently sequential algorithm (one experiment is proposed per round) and thus cannot directly exploit high-throughput (parallel) experiments. Diverse modifications to the BO framework have been proposed in the literature to enable exploitation of parallel experiments but such approaches are limited in the degree of parallelization that they can achieve and can lead to redundant experiments (thus wasting resources and potentially compromising performance). In this work, we present new parallel BO paradigms that exploit the structure of the system to partition the design space. Specifically, we propose an approach that partitions the design space by following the level sets of the performance function and an approach that exploits partially-separable structures of the performance function found. We conduct extensive numerical experiments using a reactor case study to benchmark the effectiveness of these approaches against a variety of state-of-the-art parallel algorithms reported in the literature. Our computational results show that our approaches significantly reduce the required search time and increase the probability of finding a global (rather than local) solution.
翻译:Bayesian优化(BO)是封闭环实验实验设计和黑箱优化的最有效方法之一。然而,BO的一个关键限制是,它是一种内在的顺序算法(每轮建议一个实验),因此不能直接利用高通量(平行)实验。文献中提出了对BO框架的多种修改,以便能够利用平行实验,但这类方法在它们能够实现和可能导致冗余实验的平行程度有限(从而浪费资源和可能损害性能 ) 。在这项工作中,我们提出了新的平行的BO模式,利用系统结构来分割设计空间。具体地说,我们提出了一种方法,即按照性能功能的层次来分割设计空间,以及一种利用部分可分离的性能功能结构的方法。我们使用反应堆案例研究进行广泛的数字实验,以根据文献中报道的各种状态的平行算法衡量这些方法的有效性。我们的计算结果表明,我们的方法大大缩短了所需的搜索时间,增加了寻找全球(而不是当地)解决办法的可能性。