Intelligent Genetic Algorithm and Its Application to Aerodynamic Optimization of Airplanes

Abstract

A new genetic algorithm (GA), called intelligent GA, is proposed by the inclusion of a fractional factorial design in the crossover operator to determine the best combination of design variables. That is, in the proposed GA, the chromosomes of children are generated via an intelligent crossover process with factorial experiments after completing the selection of GA operators. This gene mating process differs from the traditional GA, where the child chromosome is generally generated via randomization. Accordingly, the traditional GA incorporating the technique of fractional factorial design into the crossover operator markedly enhances the performance in terms of evolutionary efficiency. Another GA, called Taguchi-GA, that incorporates the Taguchi orthogonal arrays into the crossover operator is also developed to compare the performance of the presented intelligent GA. Four representative test cases, including two multimodal functions, a nonlinear dynamic control function, and a profile fitting of a high lift airfoil are performed via the traditional GA, micro-GA, Taguchi-GA, and present GA to assess the capacity and efficiency of the proposed GA. The computational solutions of the convergence history and optimal objective function for the test cases demonstrate that the proposed intelligent GA outperforms the other three GAs. Moreover, the presented new intelligent GA and the other three GAs are applied to the aerodynamic optimization of two airplanes, namely, an advanced defense airplane and an F-16A fighter. The redesigned wing planform is obtained by computations made with an aerodynamic flow solver, and the present GA depicts the optimal swept angle, aspect ratio, and taper ratio. The drag distribution and convergence history show that the proposed intelligent GA has a high evolutionary efficiency when applied to practical engineering problems.