Abstract: SVR(Support Vector Regression) is a kind of small sample learning method with strong robustness. It can effectively avoid‘dimension disaster' , and is introduced to the global optimization. However, the existing global optimi- nation algorithms based on SVR have several shortcomings, such as large number of evaluations, can not cope with high dimensional optimization problem and so on. We proposed a new improved global optimization algorithm DISVR based on incremental SVR model: an incremental SVR method to improve the efficiency of reconstruction process response, a new incremental I_HD sampling(I_atin Hyper-cube Sampling) to ensure an uniform distribution of samples, DIRECT search algorithm to enhance the stability and efficiency of the global search. Finally, the result of test functions suggests that the proposed method both reduce the time complexity, also effectively reduce number of source model's evalua- dons.

Key words: Global optimisation, Response surface, Support vector regression, Incremental method