This paper presents a new analysis support system for analyzing non-dominated solutions (NDSs) derived by evolutionary multi-criterion optimization (EMO). The main features of the proposed system are to use association rule analysis and to perform a multi-granularity analysis based on a hierarchical tree of NDSs. The proposed system applies association rule analysis to the whole NDSs and derives association rules related to NDSs. And a hierarchical tree is created through our original association rule grouping that guarantees to keep at least one common features. Each node of a hierarchical tree corresponds to one group consisting of association rules and is fixed in position according to inclusion relations between nodes. Since each node has some kinds of common features, the designer can analyze each node with previous knowledge of these common features. To investigate the characteristics and effectiveness of the proposed system, the proposed system is applied to the concept design problem of hybrid rocket engine (HRE) which has two objectives and six variable parameters. HRE separately stores two different types of thrust propellant unlike in the case of usual other rockets and the concept design problem of HRE has been provided by JAXA. The results of this application provided possible to analyze the trends and specifics contained in NDSs in an organized way unlike analysis approaches targeted at the whole NDSs.
[1]
Tomohiro Yoshikawa,et al.
Support to Select Satisfying Solutions Using Visualization Method in Multi-Objective Optimization Problem
,
2008
.
[2]
Shinya Watanabe,et al.
Development of a Design Support System that Can Efficiently Utilize Non-dominated Solutions
,
2008
.
[3]
Ryojiro Minato,et al.
Development of a Design Support System that Can Efficiently Utilize Non-dominated Solutions
,
2009
.
[4]
Shinya Watanabe,et al.
A new local search method with the guarantee of local Pareto optimality
,
2012,
The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems.
[5]
Ian H. Witten,et al.
Data mining - practical machine learning tools and techniques, Second Edition
,
2005,
The Morgan Kaufmann series in data management systems.
[6]
Ian C. Parmee,et al.
Designer’s Preferences and Multi—objective Preliminary Design Processes
,
2000
.
[7]
Daisuke Sasaki,et al.
Visualization and Data Mining of Pareto Solutions Using Self-Organizing Map
,
2003,
EMO.