TY - GEN
T1 - Constrained effcient global optimization with probabilistic support vector machines
AU - Basudhar, Anirban
AU - Lacaze, Sylvain
AU - Missoum, Samy
N1 - Funding Information: The support of the National Science Foundation through grants CMMI-0800117 and CMMI-1029257 is gratefully acknowledged. We would also like to acknowledge the collaboration with Dr. Jean Marc Bourinet (Institut Francais de Mecanique Avancee).
PY - 2010
Y1 - 2010
N2 - This paper presents a methodology for constrained effcient global optimization (EGO) using support vector machines (SVMs). The proposed SVM-based method has several advantages. It is more general because it is applicable to a wider variety of problems compared to current techniques. These include problems with discontinuous and binary (pass/fail) states and multiple constraints. In this paper, the objective function is ap- proximated using Kriging while the constraint boundary is approximated using an SVM classifier. The probability of misclassification by the SVM is calculated using a probabilistic support vector machine (PSVM). The existing PSVM models have certain limitations that make them unsuitable for application in the proposed methodology. Therefore, a modified PSVM model is also proposed to overcome these limitations. Several constrained EGO for- mulations are implemented and compared in this paper. The results are also compared to EGO implementations with Kriging-based constraint approximations from the literature.
AB - This paper presents a methodology for constrained effcient global optimization (EGO) using support vector machines (SVMs). The proposed SVM-based method has several advantages. It is more general because it is applicable to a wider variety of problems compared to current techniques. These include problems with discontinuous and binary (pass/fail) states and multiple constraints. In this paper, the objective function is ap- proximated using Kriging while the constraint boundary is approximated using an SVM classifier. The probability of misclassification by the SVM is calculated using a probabilistic support vector machine (PSVM). The existing PSVM models have certain limitations that make them unsuitable for application in the proposed methodology. Therefore, a modified PSVM model is also proposed to overcome these limitations. Several constrained EGO for- mulations are implemented and compared in this paper. The results are also compared to EGO implementations with Kriging-based constraint approximations from the literature.
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U2 - 10.2514/6.2010-9230
DO - 10.2514/6.2010-9230
M3 - Conference contribution
SN - 9781600869549
T3 - 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010
BT - 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010
T2 - 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, MAO 2010
Y2 - 13 September 2010 through 15 September 2010
ER -