Abstract
An ensemble of surrogate models with high robustness and accuracy can effectively avoid the difficult choice of surrogate model. However, most of the existing ensembles of surrogate models are constructed with static sampling methods. In this paper, we propose an ensemble of adaptive surrogate models by applying adaptive sampling strategy based on expected local errors. In the proposed method, local error expectations of the surrogate models are calculated. Then according to local error expectations, the new sample points are added within the dominating radius of the samples. Constructed by the RBF and Kriging models, the ensemble of adaptive surrogate models is proposed by combining the adaptive sampling strategy. The benchmark test functions and an application problem that deals with driving arm base of palletizing robot show that the proposed method can effectively improve the global and local prediction accuracy of the surrogate model.
Highlights
In the engineering design problem, computer simulation is usually applied to replace the real physics experiments
Yin [12] compared the application of a single surrogate model and an ensemble of surrogate models in groundwater restoration design optimization problems, and the results showed that the ensemble of surrogate models is more robust
Ouyang [15] used the analysis of variance method to determine the weights of ensemble of Mathematical Problems in Engineering surrogate models. e comparison results show that the proposed method can improve the prediction performance of surrogate model, and obtain a reliable solution
Summary
In the engineering design problem, computer simulation is usually applied to replace the real physics experiments. In order to prove the versatility of LEE strategy for different surrogate models, the RBF surrogate model is constructed based on the existing sample points and their response values.
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