A Design Space Exploration Method Using Artificial Neural Networks and Metamodeling

Abstract:

This paper suggests a design space exploration method using Artificial Neural Networks and metamodeling to systematically reduce the design space to a relatively small region. This method consists of three major steps. Firstly, self-organizing maps is employed to analyze design variables and objective function(s) with the original samples as preliminary reduction optimization of the initial large design space. Successively, resampling within the preliminary reduction space, clustering sample points using the fuzzy c-means clustering method with the given number of cluster, and choosing the most attractive cluster to construct kriging model and identify the design optimum within the reduced design space in the last step. The accuracy and validity of proposed methodology is proved by a heat exchanger design problem. It is found that the proposed method can intuitively capture promising design regions in which it is efficient to acquire the global or near-global desigm optimum.

• Periodical: Advanced Materials Research (Volume 544)
• Pages: 200-205
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.544.200
• Online since: June 2012
• Authors: Li Chi, Hao Bo Qiu, Zhen Zhong Chen, Li Ke
• Keywords: Design Optimization, Fuzzy Clustering, Metamodeling, Self-Organizing Maps

References

[1] Wang, G. G., & Simpson, T. (2004). Fuzzy clustering based hierarchical metamodeling for design space reduction and optimization. Engineering Optimization, 36(3), 313-335.

DOI: 10.1080/03052150310001639911

[2] Kleijnen, J. P. C. (2007). Kriging metamodeling in simulation: A review. European Journal of Operational Research, 192(3), 707-716.

DOI: 10.1016/j.ejor.2007.10.013

[3] Wang, G. G., & Shan, S. (2007). Review of metamodeling techniques in support of engineering design optimization. Journal of Mechanical Design, 29(4), 370-380.

DOI: 10.1115/1.2429697

[4] Chou, H. C., Cheng, C. H., & Chang, J. R. (2007). Extracting drug utilization knowledge using self-organizing map and rough set theory. Expert Systems with Applications, 33(2). 499-508.

DOI: 10.1016/j.eswa.2006.05.020

[5] Budayan, C., Dikmen, I., & Birgonul, M. T. (2009). Comparing the performance of traditional cluster analysis, self-organizing maps and fuzzy C-means method for strategic grouping. Expert Systems with Applications, 36(9), 11772-11781.

DOI: 10.1016/j.eswa.2009.04.022

[6] Chu, X. Z., Gao, L., Qiu, H. B., Li, W. D., & Shao, X. Y. (2010). An expert system using rough sets theory and self-organizing maps to design space exploration of complex products. Expert Systems with Applications, 37, 7364-7372.

DOI: 10.1016/j.eswa.2010.04.029

[7] Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. New York: Plenum Press.

[8] Qian, Z. G., Seepersad, C. C., & Joseph, V. R. (2006). Building surrogate models based on detailed and approximate simulation. Journal of Mechanical Design, 128, 668-677.

DOI: 10.1115/1.2179459