Black-box expensive multiobjective optimization with adaptive in-fill rules

To deal with real-life black-box expensive multiobjective optimization problems, we investigated the application of an optimization framework expanded from MOEA/D-EGO. As MOEA/D-EGO, Gaussian process modeling techniques are used to subtittute the evaluation of the problem itself. Apart from the expected improvement (EI) in-fill rule in the original MOEA/D-EGO, we define a process that adaptively selects of in-fill rule in each iteration from seven different in-fill rules, including confidence limit of different probability (CLp), probability of improvement (PI), and EI. The initial probabilities of selecting a specific in-fill rule are derived from applying the algorithm on ZDT test suite. The practical problem set-up and optimization results and lesson learned in the process are reported.