Multiobjective Bayesian Optimization for Aeroengine Using Multiple Information Sources
Ran Chen, Jingjiang Yu, Zhengen Zhao, Yuzhe Li, Jun Fu, Tianyou Chai
Abstract
Aeroengine performance optimization rem- ains significant for both efficiency and safety during specific operating conditions. Previous works usually solve this optimization problem under a single-objective optimization framework, while multiple objectives need to be optimized simultaneously. Besides, the underlying optimization process requires a variety of function evaluations, and the evaluation cost for an aeroengine is expensive. In reality, the aeroengine model has multiple information sources with different costs and accuracy. The different costs and accuracy of the multiple information sources should be traded off to guide the search for the optimal in a cost-efficient way. Therefore, we propose a multi-information-source framework for enabling efficient multiobjective Bayesian optimization. We construct the surrogate model with a multifidelity Gaussian process and choose the location–source pair with a modified acquisition function. Finally, we apply the proposed method to improve the performance indexes of the aeroengine, which confirms the efficiency of the proposed algorithm.