Improving many-objective optimizers with aggregate meta-models

In the field of multi-objective optimization there have been attempts to reduce the number of objective function evaluations by the use of surrogate models. However, in many-objective optimization, this work still has to be done to make the optimizers more practically usable. In this paper we show, that aggregate meta-models can be used even for the many-objective optimization and that they can also improve the performance of the many-objective optimizer. Moreover, meta-models are discussed from another point of view and compared to scalarization techniques in many-objective optimization. Two algorithms using our models are compared to IBEA on a set of selected benchmark functions with 5, 10, and 15 objectives.