An Artificial Neural Network Residual Kriging Based Surrogate Model for Shape and Size Optimization of a Stiffened Panel

Abstract

We perform the design optimization of a stiffened panel with curvilinear stiffeners using an artificial neural network (ANN) residual kriging based surrogate modeling approach. The ANN residual kriging based surrogate modeling involves two steps – in the first step we approximate the objective function using ANN. In the next step we use kriging to model the residue. We optimize the panel in an iterative way. Each iteration involves two steps – shape optimization and size optimization. For both shape and size optimization, we use ANN residual kriging based surrogate model. At each optimization step, we do an intial sampling and fit an ANN residual kriging model for the objective function. Then we keep updating the model using an adaptive sampling algorithm until the minimum value of the objective function converges. The comparison of the design obtained using our optimization sheme with that obtained using a traditional genetic alogorithm (GA) based optimization scheme shows satisfactory agreement. However, with this surrogate model based approach we reach optimum design with less computation effort as compared to the GA based approach which does not use any surrogate model.