Abstract: A structure optimization strategy using adaptive radial basis function artificial neural network (RBF ANN) is proposed for the large computational cost, slow convergence and easy to fall into local optimum in the multidisciplinary optimization design of gun. The high and low temperature-pressure curves of gun barrel are calculated based on the interior ballistic theory, as the load of numerical calculation of the finite element analysis model, by the secondary development of ABAQUS, which is used to obtain the optimization objectives. Then a RBF ANN is built to approximate the surrogate model for understanding the nonlinear relationships among the design variables and the optimization objectives. Penalty function method is used to solve the constraint problem, and the genetic algorithm is used to obtain current optimal solution. In the process of optimization, new sampling points are added, and the surrogate model is updated according to all the samples and their responses to improve the approximation accuracy around the local and global optimal solution. The multi-objective optimization strategy is validated by numerical test and the problem of optimization of the gun barrel structure performance to prove the efficiency of this optimization strategy. The results show that , compared to the initial design, the mass of optimized gun barrel is decreased by 6.63%, the structural stiffness is increased by 5.60%, and the maximum Von Mises stress is decreased by 6.34%. Furthermore, compared to GA without surrogate model, the number of function evaluation is decreased by 86.5%, and the total runtime is decreased by 83.3%. Key

Key words: ordnancescienceandtechnology, gunbarrel, multidisciplinarymulti-objectivestructuraloptimization, adaptiveartificialneuralnetwork, resamplingstrategy