Abstract
In the field of electric vehicles, the motor is the most commonly used permanent magnet brushless DC motor, it is essential to design optimization. A new method of genetic chaos optimization combination is proposed after analyzing the advantages and disadvantages of genetic algorithm and chaos optimization method. The chaos optimization algorithm can overcome shortcomings of failure in a wide range and improve the local searching ability and accuracy of genetic algorithm, which proves that the algorithm can converge to the global optimum with a large probability. The satisfying results are obtained by applying the method for optimizing the test function.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Naijian C, Sunan W, Hongyu D, Mingxin (2009) An improved chaos genetic algorithm and its application in parameter optimization for robot control system. Industrial electronics and applications. ICIEA 2009 4th IEEE conference on, 1940–1945
Farahani V, Vahidi B, Abyaneh HA Reconfiguration and capacitor placement simultaneously for energy loss reduction based on an improved reconfiguration method. IEEE transactions on power systems, 27(2):587–595
Arabali A, Ghofrani M, Etezadi-Amoli M et al (2013) Genetic-algorithm-based optimization approach for energy management. IEEE Trans Power Deliv 28(1):162–170
Ali Mohd H, Murata T, Tamura J (2008) Transient stability enhancement by fuzzy logic-controlled SMES considering coordination with optimal reclosing of circuit breakers. IEEE Trans Power Syst 23(2):631–640
Oh SK, Pedrycz W, Park BJ (2006) Multilayer hybrid fuzzy neural networks: synthesis via technologies of advanced computational intelligence. IEEE Transac Circ Syst I: Regul Pap 53(3):688–703
Chen SL, Hwang TT, Lin WW (2010) Randomness enhancement using digitalized modified logistic map. IEEE Trans Circuits Syst II Expr Briefs 57(12):996–1000
Zhang H, Han X, Dai S (2013) Fire occurrence probability mapping of northeast China with binary logistic regression model. IEEE J Sel Top Appl Earth Observations Remote Sens 6(1):121–127
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yan, H., Zhou, L., Liu, L. (2016). Chaos Genetic Algorithm Optimization Design Based on Permanent Magnet Brushless DC Motor. In: Jia, L., Liu, Z., Qin, Y., Ding, R., Diao, L. (eds) Proceedings of the 2015 International Conference on Electrical and Information Technologies for Rail Transportation. Lecture Notes in Electrical Engineering, vol 377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49367-0_34
Download citation
DOI: https://doi.org/10.1007/978-3-662-49367-0_34
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-49365-6
Online ISBN: 978-3-662-49367-0
eBook Packages: EnergyEnergy (R0)