Computational intelligence technology for optimal design of grid-stiffened composite structure

To overcome the difficulties of optimal design for grid-stiffened composite structures, such as multi-variables, multi-constraints, mixed discrete-continuous design variables, highly nonlinear, etc, the application of computational intelligence (CI), namely evolutionary neural networks (ENN) was considered for realizing the global nonlinear mapping between structural design parameters and structural responses. They were aimed to replace the finite element computation during an actual optimization process so as to raise the efficiency of optimization. By using genetic algorithm (GA) as the optimization procedure and the structural buckling constraint as the neural network response surface, the optimal design of grid-stiffened composite panel under axial compressive loads was studied. The results indicate that with very limited FEM sample space, the accuracy of the evolutionary buckling neural network is much higher than that of traditional BP neural network. The resulted ENN-GA algorithm proves that it can offer an efficient approach to the optimization design of large complex composite structures.