This work aims at developing a genetic algorithm (GA) to pursue the optimization of hybrid laminated composite structures. Fiber orientation (predefined ply angles), material (glass-epoxy or carbon-epoxy layer) and total number of plies are considered as design variables. The GA is chosen as an optimization tool because of its ability to deal with non-convex, multimodal and discrete optimization problems, of which the design of laminated composites is an example. First, the developed algorithm is detailed explained and validated by comparing its results to other obtained from the literature. The results of this study show that the developed algorithm converges faster. Then, the maximum stress, Tsai-Wu and Puck (PFC) failure criteria are used as constraint in the optimization process and the results yielded by them are compared and discussed. It was found that each failure criterion yielded a different optimal design.
hybrid laminated composites; optimization; genetic algorithm
