Multi-objective multi-laminate design and optimization of a Carbon Fibre Composite wing torsion box using evolutionary algorithm

The present study aims to minimize the weight of multi-laminate aerospace structures by a classical Genetic Algorithm (GA) interfaced with a CAE solver. The structural weight minimization is a multi-objective optimization problem subjected to fulfilling of strength and stiffness design requirements as well. The desired fitness function connects the multi-objective design requirements to form a single-objective function by using carefully chosen scaling factors and a weight vector to get a near optimal solution. The scaling factors normalize and the weight vector prioritizes the objective functions. The weight vector selection was based on a posteriori articulation, after obtaining a series of Pareto fronts by 3D hull plot of strength, stiffness and assembly weight data points. During the optimization, the algorithm does an intelligent laminate selection based on static strength and alters the ply orientations and thickness of laminae for faster convergence. The study further brings out the influence of mutation percentage on convergence. The optimization procedure on a transport aircraft wing torsion box has showed 29% weight reduction compared to an initial quasi-isotropic laminated structure and 54% with respect to the metallic structure.