Abstract
This paper proposes a new design support approach, which efficiently utilizes the information of many non-dominated solutions obtained from evolutionary multi-criterion optimization (EMO). The proposed approach consists of four mechanisms: grouping (clustering), reducing the number of candidates (selecting the representative solutions), dimensionality reduction, and estimation.
In this paper, we examine the characteristics and effectiveness of the proposed approach through computational experiments on a design problem of a counter rotating axial fan turbojet engine. The design target of this problem is two fans and two turbines of the engine. We handle this task as a seven-objective design problem.
