Title :
Improved genetic algorithm and its application in parameter optimization for certain aeroengine compressor guide vane regulator
Author :
Peng, Kai ; Fan, Ding ; Fu, Jiangfeng ; Zhang, Lei
Author_Institution :
Sch. of Power & Energy, Northwestern Polytech. Univ., Xi´´an, China
Abstract :
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.
Keywords :
aerospace engines; blades; compressors; convergence; fuzzy set theory; genetic algorithms; simulated annealing; GFASAGA; Parameter Optimization; aeroengine compressor guide vane regulator; evolutionary algorithms; fuzzy adaptive simulated annealing genetic algorithm with gradient direction; global search; global superlinear convergence properties; hybrid optimization algorithm; iSIGHT; improved genetic Lei algorithm; standard genetic algorithm; Blades; Convergence; Educational institutions; Genetic algorithms; Heuristic algorithms; Markov processes; Optimization; compressor guide vane regulator; fuzzy control; genetic algorithm; hybrid optimization algorithm;
Conference_Titel :
Electronics, Communications and Control (ICECC), 2011 International Conference on
Conference_Location :
Ningbo
Print_ISBN :
978-1-4577-0320-1
DOI :
10.1109/ICECC.2011.6067789
