Guoqi Xiang: a New Robust Optimization Method Based on Support Vector

We examine robust optimization problems subject to uncertainty factors in product quality and implicit performance functions, and find the accuracy of the meta-model is crucial to the success of the application of robust optimization of computationally intensive simulation models. We present a new robust optimization methodology based on support vector machine (SVM) and particle swarm algorithm (PSO) for problems that involve high dimensionality. The methodology combines experimental design theories, SVM approximation model and PSO. The applicability of the algorithm is demonstrated by using a two-bar structure system study, the performances of SVM were compared with those of polynomial regression (PR), Kriging and back-propagation neural networks (BPNN). The results showed that the prediction accuracy of SVM model was higher than those of others meta-models, and is found to be accurate and efficient for robust optimization. The optimization methodology was effectively utilized to achieve a potential performance improvement.