Summary
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 example will be given for supersonic wing design.
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© 2005 Springer-Verlag Berlin Heidelberg
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Obayashi, S. (2005). Evolutionary Multi-Objective Optimization and Visualization. In: Fujii, K., Nakahashi, K., Obayashi, S., Komurasaki, S. (eds) New Developments in Computational Fluid Dynamics. Notes on Numerical Fluid Mechanics and Multidisciplinary Design (NNFM), vol 90. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31261-7_16
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DOI: https://doi.org/10.1007/3-540-31261-7_16
Publisher Name: Springer, Berlin, Heidelberg
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