Bayesian optimization is efficient even with a small amount of data and is used in engineering and in science, including biology and chemistry. In Bayesian optimization, a parameterized model with an uncertainty is fitted to explain the experimental data, and then the model suggests parameters that would most likely improve the results. Batch Bayesian optimization reduces the processing time of optimization by parallelizing experiments. However, batch Bayesian optimization cannot be applied if the number of parallelized experiments is limited by the cost or scarcity of equipment; in such cases, sequential methods require an unrealistic amount of time. In this study, we developed pipelining Bayesian optimization (PipeBO) to reduce the processing time of optimization even with a limited number of parallel experiments. PipeBO was inspired by the pipelining of central processing unit architecture, which divides computational tasks into multiple processes. PipeBO was designed to achieve experiment parallelization by overlapping various processes of the experiments. PipeBO uses the results of completed experiments to update the parameters of running parallelized experiments. Using the Black-Box Optimization Benchmarking, which consists of 24 benchmark functions, we compared PipeBO with the sequential Bayesian optimization methods. PipeBO reduced the average processing time of optimization to about 56% for the experiments that consisted of two processes or even less for those with more processes for 20 out of the 24 functions. Overall, PipeBO parallelizes Bayesian optimization in the resource-constrained settings so that efficient optimization can be achieved.

翻译：贝叶斯优化即使在数据量较少的情况下依然高效，广泛应用于工程及生物学、化学等科学领域。在贝叶斯优化中，通过拟合具有不确定性的参数化模型来解释实验数据，随后该模型会推荐最有可能改善结果的参数。批量贝叶斯优化通过并行化实验来减少优化处理时间。然而，当并行实验数量受设备成本或稀缺性限制时，批量贝叶斯优化无法适用；在此类情况下，顺序方法则需要不切实际的时间成本。本研究开发了流水线贝叶斯优化（PipeBO），以在并行实验数量有限的情况下仍能减少优化处理时间。PipeBO的灵感来源于中央处理器架构的流水线技术，该技术将计算任务划分为多个处理阶段。PipeBO通过重叠实验的多个处理阶段来实现实验并行化，并利用已完成的实验结果来更新正在运行的并行实验参数。基于包含24个基准函数的黑盒优化基准测试集，我们将PipeBO与顺序贝叶斯优化方法进行了比较。在24个函数中的20个上，对于包含两个处理阶段的实验，PipeBO将平均优化处理时间减少至约56%；对于更多处理阶段的实验，时间减少更为显著。总体而言，PipeBO在资源受限环境下实现了贝叶斯优化的并行化，从而达成高效优化。