Using support vector machines in optimization for black-box objective functions

In many practical engineering design problems, the form of objective functions is not given explicitly in terms of design variables. Given the value of design variables, under this circumstance, the value of objective functions is obtained be real/computational experiments such as structural analysis, fluid mechanic analysis, thermodynamic analysis, and so on. Usually, these experiments are considerable expensive. In order to make the number of these experiments as few as possible, optimization is performed in parallel with predicting the form of objective functions. Response surface methods (RSM) are well known along this approach. This paper suggests to apply support vector machines (SVM) for predicting the objective functions. One of the most important tasks in this approach is to allocate sample moderately in order to make the umber of experiments as small as possible. It will be shown that the information of support vector can be used effectively to this aim. The effectiveness of our suggested method is shown through numerical examples.