A Hybrid DQ-PSO-TLBO Algorithm for Maximizing Buckling Load of Reinforced Laminated Composite Plates

In this research, an efficient mix of differential quadrature (DQ), particle swarm optimization (PSO) and teaching learning-based optimization (TLBO) methods has been introduced. First-order shear deformation based governing equations of laminated composite plates have been discretized by implementation of the DQ method. The fiber orientation and CNTs volume fraction coefficient of plate’s layers are considered as design variables of the problem. To maximize buckling load of the plate, particle swarm optimization has been used. To generate a new and efficient velocity vector of PSO, as an optimization solver of this problem, the TLBO technique concepts has been used. Efficiency and accuracy of the proposed solution procedure for the plate have been shown and effects of different parameters such as number of layers, CNTs volume fraction, height to length and length to width ratios, boundary conditions and etc. of the plate on the optimal results are investigated.