Abstract
In these decades, research on Evolutionary Multi-objective Optimization (EMO) focused on two or three objective optimization. But, multi-objective optimization with more than three objects called Many-objective Optimization is actively researched in recent years. It is reported that the performance of well-established EMO algorithms such as NSGA-II and SPEA-II rapidly degrade with increasing the number of objectives. In this research, we propose a NSGA-II-based approach that merges objectives into some groups, and compare to existent NSGA-II.
