Initial Sensitivity Optimization Algorithm for Fuzzy-C-Means Based on Particle Swarm Optimization

  • Zilong YeEmail author
  • Feng Qi
  • Jingquan Li
  • Yanjun Liu
  • Han Su
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 895)


As a local search algorithm, FCM is sensitive to the initial value. Randomly initializing the centroids or membership matrix will cause the algorithm to fall into local optimum, thus affecting the accuracy and classification results of FCM. In this paper, a fuzzy-C-Means initial sensitivity optimization algorithm, which based on particle swarm, is proposed for the above problems. In the standard PSO algorithm, the Levi flight formula is introduced to simulate global random walk to enhance particle activities and control the balance of local walking and global random walking in the distance formula by a switching parameter, finally coupled with FCM algorithm. In the experimental stage, this paper conducts clustering test and validity analysis on the accuracy and fitness variety of the algorithm through a suite of UCI standard data sets. The experimental results show that compared with the FCM algorithm and the PSO-FCM algorithm, the PSO-LF-FCM algorithm enhances the clustering accuracy and the global search performance in the later iteration of the algorithm, which implies its superior global convergence and optimal solution search ability.


Fuzzy-C-Means Particle Swarm Optimization Levy Flight 



This paper is supported by Data Network Security Assessment Strategy and State Analysis Research Information Communication and Security Technology Project.


  1. 1.
    Dai, Y., Zhao, L.: An improved, clustering algorithm based on optimized artificial fish swarm algorithm and FCM. Comput. Appl. Softw. 33(12) (2016)Google Scholar
  2. 2.
    Fu, B.: The text clustering method of FCM clustering based on chaotic oscillation particle swarm optimization. J. Hechi Univ. 35(02), 74–77 (2015)Google Scholar
  3. 3.
    Zhang, Y., Wang, L., Wi, Q.: Dynamic adaptation cuckoo search algorithm. Control Decis. 29(04), 617–622 (2014)zbMATHGoogle Scholar
  4. 4.
    Sun, W., Meng, b, Wu, X.: Improved clustering algorithm based on cuckoo search. Microelectron. Comput. 35(08), 16–20 (2018)Google Scholar
  5. 5.
    Zhang, J., Shen, L.: An improved fuzzy c-Means clustering algorithm based on shadowed sets and PSO. Comput. Intell. Neurosci. 2014, 368628 (2014)CrossRefGoogle Scholar
  6. 6.
    Zhou, K., Fu, C., Yang, S.: Fuzziness parameter selection in fuzzy c-means: the perspective of cluster validation. Sci. China (Inf. Sci.) 57(11), 252–259 (2014)Google Scholar
  7. 7.
    Samadzadegan, F.: Evaluating the potential of particle swarm optimization in clustering of hyperspectral imagery using fuzzy c-means. Asia-Pacific Chemical, Biological & Environmental Engineering Society (APCBEES). In: Proceedings of International Conference on Asia Agriculture and Animal (ICAAA 2011). Asia-Pacific Chemical, Biological & Environmental Engineering Society (APCBEES), p. 7 (2011)Google Scholar
  8. 8.
    Chen, X., Liao, J., Zhao, X., Chen, J.: On the FCM clustering method based on particle swarm optimization with tabu search. J. Hubei Univ. Technol. 28(02), 45–48 (2013)Google Scholar
  9. 9.
    Yin, H., et al.: Fish swarm algorithm with Levy flight and firefly behavior. Control Theory Appl. 35(4) (2018)Google Scholar
  10. 10.
    Zhao, Y.: Application of improved cuckoo search in parameter inversion of average elastic moduli of dam and foundation. Pearl River 39(8) (2018)Google Scholar
  11. 11.
    Zhu, C., Li, L., Guo, J.: Fuzzy clustering image segmentation algorithm based on improved cuckoo search. Comput. Sci. 44(6) (2017)Google Scholar
  12. 12.
    Silva Filho, T.M., Pimentel, B.A., Souza, R.M.C.R., Oliveira, A.L.I.: Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization. Expert. Syst. Appl. 42(17–18), 6315–6328 (2015)CrossRefGoogle Scholar
  13. 13.
    Haklı, H., Uğuz, H.: A novel particle swarm optimization algorithm with Levy flight. Appl. Soft Comput. J. 23, 333–345 (2014)CrossRefGoogle Scholar
  14. 14.
    Wang, Y.: Fuzzy C-means clustering algorithm based on particle swarm optimization. Microcomput. Appl. 37(08), 36–39+44.11 (2018)Google Scholar
  15. 15.
    Wang, J., et al.: Constrained multi-objective particle swarm optimization algorithm based on self-adaptive evolutionary learning. Control Decis. 29(10), 1765–1770 (2014)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zilong Ye
    • 1
    Email author
  • Feng Qi
    • 1
  • Jingquan Li
    • 2
  • Yanjun Liu
    • 2
  • Han Su
    • 2
  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina
  2. 2.State Grid HeBei Electric Power Supply Co. LTDShijiazhuangChina

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