  Bayesian optimization has emerged at the forefront of expensive black-box
optimization due to its data efficiency. Recent years have witnessed a
proliferation of studies on the development of new Bayesian optimization
algorithms and their applications. Hence, this paper attempts to provide a
comprehensive and updated survey of recent advances in Bayesian optimization
and identify interesting open problems. We categorize the existing work on
Bayesian optimization into nine main groups according to the motivations and
focus of the proposed algorithms. For each category, we present the main
advances with respect to the construction of surrogate models and adaptation of
the acquisition functions. Finally, we discuss the open questions and suggest
promising future research directions, in particular with regard to
heterogeneity, privacy preservation, and fairness in distributed and federated
optimization systems.
