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 …

Bayesian optimisation is a statistical method that efficiently models and optimises expensive
“black-box” functions. This review considers the application of Bayesian optimisation to …

Hypervolume is widely used as a performance indicator in the field of evolutionary
multiobjective optimization (EMO). It is used not only for performance evaluation of EMO …

Over the last three decades, a large number of evolutionary algorithms have been
developed for solving multi-objective optimization problems. However, there lacks an upto …

Only a small number of function evaluations can be afforded in many real-world
multiobjective optimization problems (MOPs) where the function evaluations are …

In many real-world scenarios, decision makers seek to efficiently optimize multiple
competing objectives in a sample-efficient fashion. Multi-objective Bayesian optimization …

The expected improvement (EI) algorithm is a very popular method for expensive
optimization problems. In the past twenty years, the EI criterion has been extended to deal …

Decomposition is a well-known strategy in traditional multiobjective optimization. However,
the decomposition strategy was not widely employed in evolutionary multiobjective …

We propose a surrogate-assisted reference vector guided evolutionary algorithm (EA) for
computationally expensive optimization problems with more than three objectives. The …

Surrogate-assisted evolutionary algorithms (SAEAs) have been developed mainly for
solving expensive optimization problems where only a small number of real fitness …