Asynchronous Bayesian optimization is a recently implemented technique that
allows for parallel operation of experimental systems and disjointed workflows.
Contrasting with serial Bayesian optimization which individually selects
experiments one at a time after conducting a measurement for each experiment,
asynchronous policies sequentially assign multiple experiments before
measurements can be taken and evaluate new measurements continuously as they
are made available. This technique allows for faster data generation and
therefore faster optimization of an experimental space. This work extends the
capabilities of asynchronous optimization methods beyond prior studies by
evaluating four additional policies that incorporate pessimistic predictions in
the training data set. Combined with a conventional greedy policy, the five
total policies were evaluated in a simulated environment and benchmarked with
serial sampling. Under some conditions and parameter space dimensionalities,
the pessimistic asynchronous policy reached optimum experimental conditions in
significantly fewer experiments than equivalent serial policies and proved to
be less susceptible to convergence onto local optima at higher dimensions.
Without accounting for the faster sampling rate, the pessimistic asynchronous
algorithm presented in this work could result in more efficient algorithm
driven optimization of high-cost experimental spaces. Accounting for sampling
rate, the presented asynchronous algorithm could allow for faster optimization
in experimental spaces where multiple experiments can be run before results are
collected.

异步贝叶斯优化通过评估四种嵌入悲观预测的策略扩展了异步优化方法的能力。在模拟环境中，这五种策略与串行采样进行了比较。在某些条件和参数空间维度下，悲观异步策略比等效的串行策略少使用显著数量的实验达到最优实验条件，并且在高维度下对局部最优值的收敛性较不敏感。在考虑更快的采样率之前，本文提出的悲观异步算法可能在高成本实验空间中实现更高效的算法驱动优化。在考虑采样率的情况下，所提出的异步算法能够在实验结果收集之前运行多个实验，从而实现更快的优化。