Multi-objective stacking sequence optimization of laminated cylindrical panels using a genetic algorithm and neural networks

A multi-objective optimization strategy for optimal stacking sequence of laminated cylindrical panels is presented, with respect to the first natural frequency and critical buckling load, using the weighted summation method. To improve the speed of the optimization process, artificial neural networks are used to reproduce the behavior of the structure both in free vibration and buckling conditions. Based on first order shear deformation theory of laminated shells, a finite element code, capable of evaluating the first natural frequency and buckling load, is prepared of which the outputs are used for training and testing the developed neural networks. In order to find the optimal solution, a genetic algorithm is implemented. Verifications are made for both finite element code results and utilization of neural networks in the optimization process. With the purpose of illustrating the optimization process, numerical results are presented for a symmetric angle-ply six layer cylindrical panel.