Parallel infill sampling is a promising approach to improve the efficiency of multi-fidelity multi-objective Bayesian optimization (MOBO). In existing literature, the number of infill samples per iteration is typically limited to 10. Additionally, the application of the multi-fidelity MOBO method in engineering optimization designs with over 100 variables is rare. To that end, a novel generalized expected improvement matrix (GEIM) criterion is proposed by using generalized reference values for the element in expected improvement matrix. Parallel infill sampling strategy based on GEIM is develo...
