A survey on kriging-based infill algorithms for multiobjective simulation optimization

Abstract This article surveys the most relevant kriging-based infill algorithms for multiobjective simulation optimization. These algorithms perform a sequential search of so-called infill points, used to update the kriging metamodel at each iteration. An infill criterion helps to balance local exploitation and global exploration during this search by using the information provided by the kriging metamodels. Most research has been done on algorithms for deterministic problem settings; only very recently, algorithms for noisy simulation outputs have been proposed. Yet, none of these algorithms so far incorporates an effective way to deal with heterogeneous noise, which remains a major challenge for future research.