Self-Organizing Maps (SOMs) have been used to visualize tradeoffs of Pareto solutions in the objective function space for engineering design obtained by Evolutionary Computation. Furthermore, based on the codebook vectors of cluster-averaged values of respective design variables obtained from the SOM, the design variable space is mapped onto another SOM. The resulting SOM generates clusters of design variables, which indicate roles of the design variables for design improvements and tradeoffs. These processes can be considered as data mining of the engineering design. Data mining examples are given for supersonic wing design and supersonic wing-fuselage design.
- Computational Fluid Dynamics
- Design Variable
- Aerodynamic Performance
- Pareto Solution
- Pitching Moment
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Kohonen T.: Self-Organizing Maps. Springer, Berlin, Heidelberg (1995)
Hollmen J.: Self-Organizing Map, http://www.cis.hut.fi/~jhollmen/dippa/node7. html, last access on October 3, 2002
Sasaki D., Obayashi S. and Nakahashi K.: Navier-Stokes Optimization of Supersonic Wings with Four Objectives Using Evolutionary Algorithm. Journal of Aircraft Vol. 39, No. 4 (2002) 621–629
Sasaki D., Yang G. and Obayashi S.: Automated Aerodynamic Optimization System for SST Wing-Body Configuration. AIAA Paper 2002-5549 (2002)
Darden, C. M.: Sonic Boom Theory: Its Status in Prediction and Minimization. Journal of Aircraft, Vol. 14, No. 6 (1977) 569–576
Fonseca C. M. and Fleming P. J.: Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. Proc. of the 5th ICGA (1993) 416–423
Obayashi S., Takahashi S. and Takeguchi Y.: Niching and Elitist Models for MOGAs. Parallel Problem Solving from Nature — PPSN V, Lecture Notes in Computer Science, Springer, Vol. 1498, Berlin Heidelberg New York (1998) 260–269
Eshelman L. J. and Schaffer J. D.: Real-Coded Genetic Algorithms and Interval Schemata. Foundations of Genetic Algorithms 2, Morgan Kaufmann Publishers, Inc., San Mateo (1993) 187–202
Eudaptics software gmbh. http://www.eudaptics.com/technology/somine4.html, last access on October 3, 2002
Vesanto, J. and Alhoniemi, E.: Clustering of the Self-Organizing Map, IEEE Transactions on Neural Networks, Vol. 11, No. 3 (2000) 586–600
Yang, G., Kondo, M. and Obayashi, S.: Multiblock Navier-Stokes Solver for Wing/Fuselage Transport Aircraft. JSME International Journal, Series B, Vol. 45, No. 1 (2002) 85–90
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Obayashi, S., Sasaki, D. (2003). Visualization and Data Mining of Pareto Solutions Using Self-Organizing Map. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_56
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