An efficient global optimization algorithm combining revised expectation improvement criteria and Kriging

Abstract

The efficient global optimization (EGO) algorithm is a kind of Bayesian optimization algorithm that uses the Kriging interpolation model and expectation improvement (EI) criteria as surrogate model and acquisition function, respectively. However, the greediness of EI criteria can lead the EGO algorithm to fall into local optima. Owing to this, revised expectation improvement (REI) criteria are proposed by introducing a balance factor to adjust the exploitation and exploration of EI criteria, and the corresponding algorithm is called the revised efficient global optimization (REGO) algorithm. In order to motivate exploration, and ensure that the computational cost is acceptable, a Latin hypercube based indicator is proposed to denote a balance factor from the viewpoint of sample distribution. Several test functions and an airfoil optimization problem are applied to verify the performance of the REGO algorithm. The results show that the REGO algorithm has acceptable computational cost, a strong ability to find global optima, and good robustness.