Abstract
A new approach for multi-objective robust design optimization was proposed and applied to a real-world design problem with a large number of objective functions. The present approach is assisted by response surface approximation and visual data-mining, and resulted in two major gains regarding computational time and data interpretation. The Kriging model for response surface approximation can markedly reduce the computational time for predictions of robustness. In addition, the use of self-organizing maps as a data- mining technique allows visualization of complicated design information between optimality and robustness in a comprehensible two- dimensional form. Therefore, the extraction and interpretation of trade-off relations between optimality and robustness of design, and also the location of sweet spots in the design space, can be performed in a comprehensive manner.
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Shimoyama, K., Lim, J.N., Jeong, S., Obayashi, S., Koishi, M. (2009). Multi-Objective Robust Optimization Assisted by Response Surface Approximation and Visual Data-Mining. In: Goh, CK., Ong, YS., Tan, K.C. (eds) Multi-Objective Memetic Algorithms. Studies in Computational Intelligence, vol 171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88051-6_7
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DOI: https://doi.org/10.1007/978-3-540-88051-6_7
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