Design optimization of moderately thick hexagonal honeycomb sandwich plate with modified multi-objective particle swarm optimization by genetic algorithm (MOPSOGA)

Design optimization of moderately thick hexagonal honeycomb sandwich plate has been investigated via employing an improved multi-objective particle swarm optimization with genetic algorithm (MOPSOGA). Based on the first-order shear deformation theory (FSDT), governing equations of the plate are obtained. The equations are solved analytically. Total weight and maximum deflection of the plate under static gravity loads are considered to be objective functions of the problem. Core height, faces thickness, cell walls thickness, vertical and inclined cell wall length and the angle between inclined cell wall and horizontal line are set to be design variables of the problem. The geometrical and failure constrains are chosen to have desirable performance and stability of the sandwich plate. In the used multi-objective optimization technique, the optimum velocity parameter, inertia weight and acceleration coefficients for next iteration of the MOPSO are obtained by employing the genetic algorithm via minimizing generational distance between the sets of dominated and non-dominated particles in the previous iteration. Efficiency and accuracy of the proposed solution procedure are demonstrated and effects of different parameters on design optimization of the plate are studied. Also, TOPSIS multi-criteria decision-making method has been selected to report appreciate results from the Pareto-front curve of the MOPSOGA.