Surrogate models serve as powerful tools for engineering application but constructing highly accurate models with limited samples remains a challenge. Inspired by this, this paper proposes a general random projection enhancement method (RPE) aimed at improving surrogate model performance. Taking least squares support vector regression (LSSVR) as an example, we conducted numerical experiments to evaluate the feasibility of the RPE method. The RPE-enhanced LSSVR demonstrates superior predictive accuracy and robustness compared to the unenhanced model and other benchmark models. The results show that the RPE method can not only enhance the predictive accuracy of the model but also improve the robustness of the model. Meanwhile, analysis of optimization experiments indicated that the enhanced LSSVR model obtained better solutions compared to the unenhanced LSSVR model. Even when extended to high-dimensional problems, the RPE method remains effective. Furthermore, the applicability of RPE method extends from the LSSVR model to other models, as demonstrated by the enhancement of the extreme learning machine, regression kriging, and radial basis function neural network. More importantly, the feasibility of the RPE method is also verified in engineering problems, obtaining the same results. This method offers a reliable alternative for real-world engineering optimization problems.
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