Clustering-based statistical global optimization

A global optimization algorithm based on statistical modeling of the objective function and a rectangular decomposition of the feasible region, where an analogue to the probability of improvement is used as a selection criterion, is extended to speed up convergence. Measurement clusters are determined using a clustering algorithm, a local descent is performed to efficiently find the local minimizers and the explored regions are masked out from further processing. The performance improvement is demonstrated by numerical experiments.