Improved genetic algorithm and its application in parameter optimization for certain aeroengine compressor guide vane regulator

An improved genetic algorithm (Fuzzy Adaptive Simulated Annealing Genetic Algorithm with Gradient direction, GFASAGA) will be proposed in this paper, whose global superlinear convergence properties was analyzed by means by Markov chain etc. Certain fuzzy aeroengine compressor guide vane controller parameters of the regulator were optimized by GFASAGA, standard genetic algorithm (SGA) and customized hybrid optimization algorithm in iSIGHT comparatively, then simulation results show that: the improved genetic algorithm is of good characteristics, such as global search, evolutionary rapidity and so on; the ultimate guide vane regulator formed by semi physical simulation is provided with good static and dynamic characteristics.