Cooperative Bayesian optimization with hybrid grouping strategy and sample transfer for expensive large-scale black-box problems

Expensive large-scale optimization problems are difficult to solve owing to their high complexity and computational costs. Bayesian optimization is an effective method for dealing with low-dimensional, costly problems. This study extends Bayesian optimization to expensive large-scale optimization problems. In the study, the large-scale problem is decomposed into a series of low-dimensional