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A Data Mining-Based Strategy for Direct Multidisciplinary Optimization
ISSN: 1946-3979, e-ISSN: 1946-3987
Published April 14, 2015 by SAE International in United States
Citation: Xu, H., Chuang, C., and Yang, R., "A Data Mining-Based Strategy for Direct Multidisciplinary Optimization," SAE Int. J. Mater. Manf. 8(2):357-363, 2015, https://doi.org/10.4271/2015-01-0479.
One of the major challenges in multiobjective, multidisciplinary design optimization (MDO) is the long computational time required in evaluating the new designs' performances. To shorten the cycle time of product design, a data mining-based strategy is developed to improve the efficiency of heuristic optimization algorithms. Based on the historical information of the optimization process, clustering and classification techniques are employed to identify and eliminate the low quality and repetitive designs before operating the time-consuming design evaluations. The proposed method improves design performances within the same computation budget. Two case studies, one mathematical benchmark problem and one vehicle side impact design problem, are conducted as demonstration.
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