Multivariate Optimization of Fiber Reinforced Laminate Using Ant Colony Optimization Algorithm

Fiber reinforced laminate design is a challenging problem in the field of composite laminates. It provides us a systematic way to design the laminates of desired properties while conveniently incorporating the thick-ness and mass constraints. In this paper, we pursue the multivariate graphite fiber reinforced laminate design problem using Ant Colony Optimization (ACO) algorithm. Classical lamination theory is used to determine mid-plane strains, curvatures and stresses in individual lamina under applied biaxial loading conditions. The fiber orientations, lamina thickness, number of layers and fiber volume fractions of lamina are considered as the optimization variables. Failure of the lamina is analyzed by Tsai–Wu failure criterion. Objective of the study is to maximize the load carry capacity of the composite laminate structure and minimize the areal mass density under multivariate/multiobjective optimization.