Sequential ensemble optimization based on general surrogate model prediction variance and its application on engine acceleration schedule design

The Efficient Global Optimization (EGO) algorithm has been widely used in the numerical design optimization of engineering systems. However, the need for an uncertainty estimator limits the selection of a surrogate model. In this paper, a Sequential Ensemble Optimization (SEO) algorithm based on the ensemble model is proposed. In the proposed algorithm, there is no limitation on the selection of an individual surrogate model. Specifically, the SEO is built based on the EGO by extending the EGO algorithm so that it can be used in combination with the ensemble model. Also, a new uncertainty estimator for any surrogate model named the General Uncertainty Estimator (GUE) is proposed. The performance of the proposed SEO algorithm is verified by the simulations using ten well-known mathematical functions with varying dimensions. The results show that the proposed SEO algorithm performs better than the traditional EGO algorithm in terms of both the final optimization results and the convergence rate. Further, the proposed algorithm is applied to the global optimization control for turbo-fan engine acceleration schedule design.