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http://dx.doi.org/10.5370/JEET.2008.3.2.207

A Robust and Computationally Efficient Optimal Design Algorithm of Electromagnetic Devices Using Adaptive Response Surface Method  

Zhang, Yanli (School of EE, Shenyang University of Technology)
Yoon, Hee-Sung (School of ECE, Chungbuk National University)
Shin, Pan-Seok (Dept. of EE, Hongik University)
Koh, Chang-Seop (School of Electrical & Computer Engineering, Chungbuk National University)
Publication Information
Journal of Electrical Engineering and Technology / v.3, no.2, 2008 , pp. 207-212 More about this Journal
Abstract
This paper presents a robust and computationally efficient optimal design algorithm for electromagnetic devices by combining an adaptive response surface approximation of the objective function and($1+{\lambda}$) evolution strategy. In the adaptive response surface approximation, the design space is successively reduced with the iteration, and Pareto-optimal sampling points are generated by using Latin hypercube design with the Max Distance and Min Distance criteria. The proposed algorithm is applied to an analytic example and TEAM problem 22, and its robustness and computational efficiency are investigated.
Keywords
Adaptive Response Surface Method; Latin Hypercube Design; Optimal Design; Pareto Optimization;
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1 L. Wang and D. Lowther, "Selection of approximation models for electromagnetic device optimization," IEEE Trans. on Magn., vol. 42, no. 4, pp. 1227-1230, Apr. 2006   DOI   ScienceOn
2 S. L. Ho et al., "A RSM based on improved compactly supported radial bases function and its application to rapid optimizations of electromagnetic devices," IEEE Trans. on Magn., vol. 41, no. 6, pp. 2111-2117, Jun. 2005   DOI   ScienceOn
3 Y. Yao, C. S. Koh, S. Yang, and G. Ni, "A Global Optimization algorithm based on $C^1$ piecewise response surface patches," IEEE Trans. on Magn., vol. 43, no. 4, pp. 1629-1632, Apr. 2007   DOI   ScienceOn
4 B. Brandstaetter, "SMES optimization benchmark, TEAM problem 22, 3 parameter problem," http://www.igte.tu-graz.ac.at/archive/team_new/team3.php
5 S. Rippa, "An algorithm for selecting a good value for the parameter c in radial basis function interpolation," Advances in Computational Mathematics, vol. 11, pp. 193-210, 1999   DOI
6 X. K. Gao et al., "Robust design for torque optimization using response surface methodology," IEEE Trans. on Magn., vol. 38, no. 2, pp. 1141-1144, Mar. 2002   DOI   ScienceOn
7 D. Han and A. Chatterjee, "Adaptive response surface modeling-based method for analog circuit sizing," Proceedings of IEEE International SOC Conference, 2004, pp. 109-112
8 R. H. C. Takahashi, J. A. Vasconcelos, J. A. Ramirez, and L. Krahenbuhl, "A multiobjective methodology for evaluating genetic operators," IEEE Trans. Magn., vol. 39, pp. 1321-1324, May 2003   DOI   ScienceOn
9 S. L. Ho, P. H. Ni, S. Y. Yang, and K. F. Wong, "An adaptive interpolating moving least squares response surface model applied to the design optimizations of electromagnetic devices," Proc. of CEFC, 2006, pp. 59
10 J. R. Koehler and A. B. Owen, Computer Experiments, Handbook of Statistics, Elsevier Science, New York, 1996, pp. 261-308
11 J. Webb, "Construction of device performance models using adaptive interpolation and sensitivities," IEEE Trans. on Magn., vol. 41, no. 5, pp. 1768-1771, May 2005   DOI   ScienceOn