Abstract
Surrogate models have been widely used for solving computationally expensive multi-objective optimization problems (MOPs). The efficient global optimization (EGO) algorithm, a Bayesian approach to surrogate-assisted optimization, has become very popular in surrogate-assisted evolutionary optimization. In this paper, we propose an adaptive Bayesian approach to surrogate-assisted evolutionary algorithm to solve expensive MOPs. The main idea is to tune the hyperparameter in the acquisition function according to the search dynamics to determine which candidate solutions are to be evaluated using the expensive real objective functions. In addition, the sampling selection criterion switches between an angle based distance and an angle-penalized distance over the course of optimization to achieve a better balance between exploration and exploitation. The performance of the proposed algorithm is examined on a set of benchmark problems and an airfoil design optimization problem using a maximum of 300 real fitness evaluations. Our experimental results show that the proposed algorithm is competitive compared to four popular multi-objective evolutionary algorithms.