Permutation genetic algorithm for stacking sequence design of composite laminates Original Research Article

Stacking sequence design of a composite laminate with a given set of plies is a combinatorial problem of seeking an optimal permutation. Permutation genetic algorithms optimizing the stacking sequence of a composite laminate for maximum buckling load are studied. A new permutation GA named gene–rank GA is developed and compared with an existing Partially Mapped Permutation GA, originally developed for solving the travelling salesman problem. The two permutation GAs are also compared with a standard non-permutation GA. It is demonstrated through examples that the permutation GAs are more efficient for stacking sequence optimization than a standard GA. Repair strategies for standard GA and the two permutation GAs for dealing with constraints are also developed. It is shown that using repair can significantly reduce computation cost for both standard GA and permutation GA.