(624f) Exploiting High-Throughput Experiments in Bayesian Optimization
AIChE Annual Meeting
Thursday, November 17, 2022 - 2:05pm to 2:24pm
Bayesian optimization (BO) has proven to be one of the most effective machine learning (ML) algorithms for DoE . BO is widely used in applications such as hyper-parameter tuning of ML models and reinforcement learning, and it has been shown to be a sample-efficient learning algorithm . Of particular interest to researchers is the flexibility of BO; specifically, this approach is capable of accommodating both continuous and discrete parameters . Unfortunately, the inherently sequential nature of BO makes it incompatible with DoE on HTE platforms. Ad-hoc modifications to the BO algorithm that would give it parallelization capabilities have been developed [9, 10, 11], and experimental results have demonstrated that these approaches can provide better performance than sequential BO . However, these approaches are limited in the degree of parallelization that can be achieved, and can increase the complexity of the BO algorithm, making it slower and more difficult to implement.
In this work, we propose strategies for parallelizing BO algorithms and with this exploit HTE platforms. These strategies are centered around modifications to the optimization routine of the acquisition function (AF), which serves as the decision-making mechanism for BO. Our approaches are focused around new and effective ways for partitioning the parameter space, allowing the AF to select multiple sampling points in tandem; we then assign a module to explore and optimize a specified objective in each partition. By processing the obtained data into a central model that is shared with each module, we ensure that they are able to observe global rather than local trends which further improves and accelerates the optimization routine. The methods we propose are scalable to any desired number of experiments, fully parallel, and designed to prevent redundant sampling. We apply our approach to a case study for a chemical reactor network where the aim is to select the temperature of each reactor that will minimize the yearly operating cost of the system. In addition to sequential BO, we also compare the performance of our parallel BO algorithm with existing parallelization techniques found in the literature such as Hyperspace , NxMC , and AF optimization over a set of exploratory parameters .
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