论文信息 - STRUCTURAL OPTIMIZATION USING FEMLAB AND SMOOTH SUPPORT VECTOR REGRESSION

STRUCTURAL OPTIMIZATION USING FEMLAB AND SMOOTH SUPPORT VECTOR REGRESSION

An effective algorithm for structural optimization is proposed in this paper. In the proposed method, the optimum design is achieved sequentially based on the surrogate model constructed by smooth support vector regression (SSVR). The proposed research work uses Quasi Monte Carlo (QMC) technique for the selection of training data in the design space. SSVR using a radial basis function kernel is used to build the metamodel for structural optimization. The structural responses are evaluated by a commercial finite element package, FEMLAB (recently renamed as COMSOL). Several examples are presented to illustrate the effectiveness of the proposed approach.

Divija Odapally

[1] Yuh-Jye Lee,et al. epsilon-SSVR: A Smooth Support Vector Machine for epsilon-Insensitive Regression , 2005, IEEE Trans. Knowl. Data Eng..

[2] Panu Erästö,et al. Support Vector Machines - Backgrounds and Practice , 2001 .

[3] Boonserm Kijsirikul,et al. Evolutionary strategies for multi-scale radial basis function kernels in support vector machines , 2005, GECCO '05.

[4] L. Völgyesi,et al. SUPPORT VECTOR REGRESSION VIA MATHEMATICA , 2005 .

[5] Yuh-Jye Lee,et al. SSVM: A Smooth Support Vector Machine for Classification , 2001, Comput. Optim. Appl..

[6] T. Simpson,et al. Analysis of support vector regression for approximation of complex engineering analyses , 2005, DAC 2003.

[7] Vladimir Vapnik,et al. Statistical learning theory , 1998 .

[8] Chih-Jen Lin,et al. A Practical Guide to Support Vector Classication , 2008 .

[9] Singiresu S. Rao. Engineering Optimization : Theory and Practice , 2010 .

[10] Bernhard Schölkopf,et al. A tutorial on support vector regression , 2004, Stat. Comput..

[11] Wolfgang Jank,et al. Quasi-Monte Carlo sampling to improve the efficiency of Monte Carlo EM , 2004, Comput. Stat. Data Anal..

[12] Uri Kirsch,et al. Structural Optimization: Fundamentals and Applications , 1993 .

[13] S. Gunn. Support Vector Machines for Classification and Regression , 1998 .

[14] J. Korvink,et al. Structure topology optimization: fully coupled level set method via FEMLAB , 2005 .