Multiobjective Bayesian optimization on HPGR
When applying Bayesian optimization (BO) algorithms which utilizes Gaussian processes (GP) models, several components can be adapted to suit different systems and optimization objectives. In this paper, we explore and evaluate a set of such modifications to tailor the algorithm for a high-pressure grinding rolls (HPGR) system. The system is represented by a steady-state simulator modeling the HPGR
