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Specific Dimension Stagnation Optimal Mutation of Particle Swarm Optimization Algorithm

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Recent Advances in Computer Science and Information Engineering

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 125))

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Abstract

A new specific dimension stagnation optimal mutation of PSO algorithm (SMPSO) is proposed to overcome the shortcomings of the standard PSO algorithm such as premature convergence, easily trapping into a local optimum and lowly searching precision. The presented algorithm can prevent the iterative process from falling into the local extremum. Five test functions are employed to test performance of the algorithm. Comparisons with some existing algorithms show that the new algorithm has a good performance.

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References

  1. Chen, J.Y., Yang, D.Y., Lu, J.: Self-adaptive crossover particle swarm optimization based on penalty mechanism. Computer Science 37, 249–254 (2010) (in Chinese)

    Google Scholar 

  2. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proc. of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43 (1995)

    Google Scholar 

  3. Eberhart, R., Shi, Y.: Particle swarm optimization: developments, applications and resources. In: Proc. of Congress on Evolutionary Computation, Seoul, South Korea, pp. 81–86 (2001)

    Google Scholar 

  4. Higashi, N., Iba, H.: Particle swarm optimization with gaussian mutation. In: Proc. of Swarm Intelligence Symposium (2003)

    Google Scholar 

  5. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. of IEEE International Conference on Neural Networks, Perth, WA, Australia, pp. 1942–1948 (1995)

    Google Scholar 

  6. Shi, Y., Eberhart, R.: Empirical study of particle swarm optimization. In: Proc. of Congress on Evolutionary Computation, Washington, DC, USA, pp. 1945–1950 (1999)

    Google Scholar 

  7. Yang, C.H., Gu, L.S., Gui, L.S.: Particle swarm optimization algorithm with adaptive mutation. Computer Engineering 16, 188–190 (2008) (in Chinese)

    Google Scholar 

  8. Zhao, Q.Y., Pan, B.C., Zheng, S.L.: Mutational particle swarm optimization algorithm based on warm evaluation. Computer Engineering and Applications 45, 57–59 (2009) (in Chinese)

    Google Scholar 

  9. Zhu, Y.P.: Particle swarm optimization algorithm witn optimal mutation. Computer Era 11, 37–39 (2008) (in Chinese)

    Google Scholar 

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Authors and Affiliations

  1. Institute of Applied Mathematics, Liaoning University of Science and Technology, Anshan, Liaoning Province, China

    Daqing Zhang & Ling Wang

Authors
  1. Daqing Zhang
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  2. Ling Wang
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Corresponding author

Correspondence to Daqing Zhang .

Editor information

Editors and Affiliations

  1. , College of Communication Engineering, Jilin University, Room 313, Building No.1, Changchun, Nanhu Avenue 5372, Jilin, 130012, China, People's Republic

    Zhihong Qian

  2. , Department of Electrical Engineering, The University of Mississippi, Anderson Hall 314, Mississippi, 38677, Mississippi, USA

    Lei Cao

  3. , Department of Electrical and Computer En, Naval Postgraduate School, Rm. 452 Spanagel Bldg. 232, Dyer Road 833, Monterey, 93943-5121, California, USA

    Weilian Su

  4. , Faculty of Computing, London Metropolitan University, Holloway Road 166-220, London, N7 8DB, United Kingdom

    Tingkai Wang

  5. , College of Software, Changchun University of Science and Tech, Changchun, Jilin, 130022, China, People's Republic

    Huamin Yang

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© 2012 Springer-Verlag GmbH Berlin Heidelberg

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Zhang, D., Wang, L. (2012). Specific Dimension Stagnation Optimal Mutation of Particle Swarm Optimization Algorithm. In: Qian, Z., Cao, L., Su, W., Wang, T., Yang, H. (eds) Recent Advances in Computer Science and Information Engineering. Lecture Notes in Electrical Engineering, vol 125. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25789-6_4

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  • DOI: https://doi.org/10.1007/978-3-642-25789-6_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25788-9

  • Online ISBN: 978-3-642-25789-6

  • eBook Packages: EngineeringEngineering (R0)

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