Aeroelastic Optimization of Composite Wing Based on Improved Adaptive Genetic Algorithm
نویسندگان
چکیده
Having the constant population size and crossover/mutation probability, standard genetic algorithm (SGA) has such disadvantages as premature convergence, low stability and optimization efficiency for large design variables situation. This paper presents an improved adaptive genetic algorithm (IAGA), which adjusts the population size and the crossover/mutation probability adaptively and linearly, as well as integrating the IAGA running in a high performance parallel computing platform with high efficiency. The IAGA has been tested on an aeroelastic optimization of a composite wing. The case shows that the IAGA has realized improving the premature convergence, stability and optimization efficiency.
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