Adaptive Bayesian Optimization for Fast Exploration Under Safety Constraints

Abstract

The industrial field faces the problem of process optimization by finding the factors affecting the yield of the process and controlling them appropriately. However, due to limited resources such as time and money, optimization is performed using a low evaluation budget. In addition, for process stability, the lower limit of the yield is set so that the yield must be maintained above this limit during optimization. Bayesian Optimization (BO) can be an effective solution in acquiring optimal samples that satisfy a safety constraint given a low evaluation budget. However, many existing BO algorithms have some limitations such as significant performance degradation due to model misspecification, and high computational load. Thus, we propose a practical safe BO algorithm, A-SafeBO, that effectively reduces performance degradation due to model misspecification using only a limited evaluation budget. Additionally, our algorithm performs computations for a large number of observations and high-dimensional input spaces by using Ensemble Gaussian Processes and Safe Particle Swarm Optimization. Here, we also propose a new acquisition function that leads to a wider exploration even under the constraint of safety. This will help deviate from the local optimum and achieve a better recommendation. Our algorithm empirically guarantees convergence and performance through evaluations on several synthetic benchmarks and a real-world optimization problem.