Surrogate models have been widely used for solving computationally expensive multi-objective optimization problems (MOPs). The e�cient global optimiza- tion (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 to be evaluated using the expensive real objective functions. In addition, the sampling selection criterion switches be- tween an angle based distance and an angle-penalized distance over the course of optimization to achieve a better balance between exploration and exploitation.
If you found AB-MOEA useful, we would be grateful if you cite the following reference: Wang, Xilu, Yaochu Jin, Sebastian Schmitt, and Markus Olhofer. "An adaptive Bayesian approach to surrogate-assisted evolutionary multi-objective optimization." Information Sciences 519 (2020): 317-331.