An Ant Colony Optimization algorithm for stacking sequence design of composite laminates

The study reported in this paper explores the potential of Ant Colony Optimization (ACO) metaheuristic for stacking sequence optimization of composite laminates. ACO is a recently proposed population-based search approach able to deal with a wide range of optimization problems, especially of a combinatorial nature, and inspired by the natural foraging behavior of ant colonies. ACO search processes, in which the activities of real ants are simulated by means of artificial agents that communicate and cooperate through the modification of the local environment, were implemented in a specifically developed numerical algorithm aimed at the lay-up optimization (based on a strain energy criterion) of laminated plates subject to in-plane and out-of-plane loads. Numerical analyses were conducted to investigate the quality and reliability of the metaheuristic search procedure under various load cases, geometry configurations and constraint conditions. The analyses indicated that the proposed ACO algorithm is able to achieve reasonably good solutions within very few iterations, and extremely high-quality solutions within a limited number of runs, with respect to the total number of possible solutions, for both uncostrained and constrained optimization lay-up problems. The results obtained during the investigation point out the robustness and effectiveness of the procedure and suggest the use of ACO-based search techniques as practical design tools for laminate lay-up configuration.