Surrogate-based multi-objective optimization of firing accuracy and firing stability for a towed artillery

Abstract

Determination of general parameters is one of the most essential tasks in optimal structural designs to increase firing accuracy or firing stability, since they are two of the most important performance requirements in artillery designs. This paper presents a multi-objective optimization approach, based on multidisciplinary agent model method. An experiment verified artillery multi-body rigid-flexible coupled dynamic model was first presented. Sample library was generated by optimal Latin hypercube design algorithm and this dynamic model. Then a radial basis function-back propagation neural (RBF-BP series combine) network model was developed to predict firing parameters, used the sample library to train and test the validation of developed neural network model. Finally, an application case was given by NSGA-II and the max-min criterion, its results demonstrate the effectiveness of our method through comparing with its original value.