@article {pub4083,
	title = {An Adaptive Bayesian Approach to Surrogate-Assisted Evolutionary Multi-objective Optimization},
	author = {Xilu Wang AND Yaochu Jin AND Sebastian Schmitt AND Markus Olhofer},
	year = {2020},
	month = {May},
	abstract = {Surrogate models have been widely used for solving computationally expensive
multi-objective optimization problems (MOPs). The efficient 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 selection criterion switches between the
angle-penalized distance and the angle-based selection over the course of op-
timization to achieve a better balance between exploration and exploitation.
The performance of the proposed algorithm is examined on a set of benchmark
problems using a maximum of 300 real fitness evaluations. Our experimental re-
sults show that the proposed algorithm is competitive compared to four popular
multi-objective evolutionary algorithms.},
	publisher = {Elsevier},
	journal = {Information Sciences}
}