A comparison of three population-based optimization techniques for the design of composite sandwich materials

Three population-based optimization techniques — particle swarm optimization, ant colony optimization, and simulated annealing — are applied to the multi-objective design of sandwich materials. The formulation of the algorithms is described, along with metrics for quantifying their performance. For the case study considered of a sandwich beam in bending, it is shown that the ant colony technique out-performs the other two algorithms with respect to identifying optimal geometries, materials, and lay-ups for a stiff sandwich in which low mass and low cost are the design objectives. The particle swarm and simulated annealing algorithms were found to have particular difficulties in handling the optimization of multi-ply oriented fiber-reinforced polymer facing laminates.