Research on Feature Selection Based on Improved Particle Swarm Optimization

Abstract:

Article Preview

Feature selection is one of key technologies for fault diagnosis. Especially for high dimensional data, Feature selection can not only find the feature subset with sufficient information, but also improve the classification accuracy and efficiency. In order to decrease the number of diagnosis parameter in fault diagnosis of Liquid-propellant Rocket Engine, the paper proposes one feature selection method based on improved particle swarm optimization, the method applies the quantum evolution thoughts to PSO. The particle is restricted in the range from -π/2 to 0, so the particle can correspond to the quantum angle. The parameter optimization function is designed. The improved algorithm can decrease the number of parameter in fault diagnosis of Liquid-propellant Rocket Engine from 25 to 6.

Info:

Periodical:

Advanced Materials Research (Volumes 591-593)

Edited by:

Liangchi Zhang, Chunliang Zhang, Jeng-Haur Horng and Zichen Chen

Pages:

2651-2654

Citation:

G. Q. Wang et al., "Research on Feature Selection Based on Improved Particle Swarm Optimization", Advanced Materials Research, Vols. 591-593, pp. 2651-2654, 2012

Online since:

November 2012

Export:

Price:

$41.00

[1] James Kennedy and Russell Eberhart. IEEE Int'1 Conference on Neural Networks, Perth , Australia, 1995: 1942-(1948).

[2] James Kennedy, Russell Eberhart. IEEE Conference on Computational Cybernetics and Simulation, Piscataway, 1997: 414-4108.

[3] Farzaneh Afshinmanesh, Alireza Marandi and Ashkan Rahimi-Kian. The International Conference on Computer as a Tool, EUROCON 2005: 217-220.

[4] Alireza Marandi and Farzaneh Afshinmanesh. IEEE Congress on Evolutionary Computation, Vancouver, BC, 2006: 3212-3218.

[5] J Liu, J San and Wb Xu. Quantum-behaved Particle Swarm Optimization with mutation operator[J], IEEE Tools with Artificial Intelligence, 2005, pp.237-240.

DOI: https://doi.org/10.1109/ictai.2005.104

[6] Shi Y.H. Eberhar, R C. IEEE International Conference on Evolutionary Computatio, Anchorage , 1998: 68-73.

[7] EBERHART C and SHI Y. 2000 International Congress on Evolutionary Computation(San Diego, Calfornia, NJ, (2000).

[8] Lc Jiao, Hf Du, F Liu. Immune Optimization Computation, learning and recognition[M], Edition No. 1, publishing company of science, 2006, pp.184-192.

[9] Han K H. Quantum-inspired evolutionary algorithms for a class of combinatorial optimization[J]. IEEE Transactions on Evolutionary Computation, IEEE Press, 2002, 6(6): pp.580-593.

DOI: https://doi.org/10.1109/tevc.2002.804320