Sequential Reliability-Based Optimization with Support Vector Machines
Traditional reliability-based design optimization (RBDO) is either computational intensive or not accurate enough. In this work, a new RBDO method based on Support Vector Machines (SVM) is proposed. For reliability analysis, SVM is used to create a surrogate model of the limit-state function at the Most Probable Point (MPP). The uniqueness of the new method is the use of the gradient of the limit-state function at the MPP. This guarantees that the surrogate model not only passes through the MPP but also is tangent to the limit-state function at the MPP. Then Importance Sampling (IS) is used to calculate the probability of failure based on the surrogate model. This treatment significantly improves the accuracy of reliability analysis. For optimization, the Sequential Optimization and Reliability Assessment (SORA) is employed, which decouples deterministic optimization from the SVM reliability analysis. The decoupling makes RBDO more efficient. The two examples show that the new method is more accurate with a moderately increased computational cost.
Y. Wang et al., "Sequential Reliability-Based Optimization with Support Vector Machines," Jisuan Lixue Xuebao/Chinese Journal of Computational Mechanics, Editorial Office of Chinese Journal of Computational Mechanics, Jan 2013.
The definitive version is available at http://dx.doi.org/10.7511/jslx201304005
Mechanical and Aerospace Engineering
Article - Journal
© 2013 Editorial Office of Chinese Journal of Computational Mechanics, All rights reserved.