Statistical optimization of expensive multi-response black-box functions

Assume that a set of $P$ process parameters $p_i$, $i=1,\dots,P$, determines the outcome of a set of $D$ descriptor variables $d_j$, $j=1,\dots,D$, via an unknown functional relationship $\phi: \mathbf{p} \mapsto \mathbf{d}, \, \mathbb{R}^{P} \to \mathbb{R}^{D}$, where $\mathbf{p}=(p_1,\dots,p_{P})$, $\mathbf{d}=(d_1,\dots,d_{D})$. It is desired to find appropriate values $\mathbf{\hat p} = ({\hat p}_1,\dots, {\hat p}_P)$ for the process parameters such that the corresponding values of the descriptor variables $\phi (\mathbf {\hat p})$ are close to a given target $\mathbf d^*=(d^*_1,\dots,d^*_D)$, assuming that at least one exact solution exists. A sequential approach using dimension reduction techniques has been developed to achieve this. In a simulation study, results of the suggested approach and the algorithms NSGA-II, SMS-EMOA and MOEA/D are compared.