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

fYear

2011

fDate

9-11 Sept. 2011

Firstpage

2097

Lastpage

2100

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Communications and Control (ICECC), 2011 International Conference on

Conference_Location

Ningbo

Print_ISBN

978-1-4577-0320-1

Type

conf

DOI

10.1109/ICECC.2011.6067789

Filename

6067789