Abstract
In the cooling fan optimization, there are many local minima near the optima, which improves the accuracy requirement of the Kriging model. Due to unexpected prediction errors caused by some deceptive samples, the model exploration capability of the traditional method is not enough. To overcome this problem, an adaptive Kriging method based on the trust index is proposed in this paper. By considering the sample distribution and region nonlinearity, the trust index is used to evaluate the reliability of the samples, which can enhance the sampling strategy for new candidates. Several classic test functions with many local minima are employed to verify the effectiveness of the proposed method. Further, the method is used to optimize the cooling fan, and the simulation result shows that the performance of the optimization objective is significantly increased.
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