Augmented radial basis function metamodel method based on multi-strategy

To improve the predicted accuracy and calculating efficiency of Radial Basis Function (RBF) metamodel, an Augmented Radial Basis Function (ARBF) metamodel method based on multi-strategy was proposed. Local intensive adding-points strategy, global uniform selecting-points strategy and minimum distance filtering strategy were applied to construct RBF metamodel, and RBF model was established by obtaining initial samples with Latin hypercube sampling. Then the optimal solution was obtained with Seven-spot Ladybird Optimization(SLO)algorithm. To balance the exploration and exploitation of the proposed method, the training samples were obtained by combining the local with the global strategies based on known samples. Afterwards, the minimum distance filtering strategy was used to filter the current samples so as to guide the model to predict precisely. Simulation experiments were carried out using numerical and engineering optimization examples, the results showed that ARBF was more accurate and efficient. Especially for the engineering problem, the result relatived to the theoretical optimal solution was only 0.01%, the calling number of metamodel with ARBF was decreased by 33.10%,66.19% and 72.78% compared to other three methods. © 2019, Editorial Department of CIMS. All right reserved.