Adaptive Way of Particle Swarm Algorithm Employing the Fuzzy Logic

  • Rajesh Eswarawaka
  • C Subash Chandra
  • Vadali SrinivasEmail author
  • Kanumuri Viswas
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1057)


The Image Swarm Intelligence calculations, in numerous enhancement issues, have always filled a need of worldwide hunt strategy. One of the issues went up against amid advancement is bunching issue. Contribution for a bunching procedure is an arrangement of information, which are then composed into various sub-gatherings. Current investigations have suggested that divided or isolated bunching calculations are being more fitted for grouping of wide and colossal data objects or datasets. A standout among the most and best regular partitional bunching calculations is k-means. K-implies calculation demonstrates a more quick union than PSO, however, then against nearby ideal territory is for the most part caught relying upon the arbitrary estimations of introductory centroids. A proficient crossbreed technique is displayed in this paper, specifically molecule swarm improvement with fluffy rationale or versatile molecule swarm enhancement (APSO) to determine information grouping issue. The PSO calculation finds a decent or close ideal arrangement in sensible time, however, its introduction was upgraded by seeding the underlying swarm with fuzzifier work. The versatile fluffy molecule swarm enhancement calculation (APSO) is contrasted and k-implies utilizing all-out execution time and bunching bunch blunder. It is found that the aggregate execution time for APSO technique outflanks the k-implies and had higher arrangement quality as far as bunching bunch blunder.


Hybridization Fuzzy logic Particle swarm optimization K-means 


  1. 1.
    Tan, P., Steinbach, M., Kumar, V.: Introduction to Data Mining, 1st edn. Addison Wesley, May 2005Google Scholar
  2. 2.
    Baraldi, A., Blonda, P.: A survey of fuzzy clustering algorithms for pattern recognition. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 29(6), 778–785, Dec 1999Google Scholar
  3. 3.
    Silic, A., Moens, M.-F., Zmak, L., Basic, B.: Comparing Document Classification Schemes Using k-means Clustering, vol. 5177, pp. 615–624 (2008)Google Scholar
  4. 4.
    MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. Univ. of Calif. Press (1967)Google Scholar
  5. 5.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), September 1999Google Scholar
  6. 6.
    Fleischer, M.: Foundations of Swarm Intelligence: From Principles to Practice. Swarming: Network Enabled C4ISR 2003 By Mark FleischerGoogle Scholar
  7. 7.
    Hashim, H.A., El-Ferik, S., Ayinde, B.O., Abido, M.A.: Optimal Tuning of Fuzzy Feedback Filter for L1 Adaptive Controller using Multi-Objective Particle Swarm Optimization for Uncertain Nonlinear MIMO Systems. arXiv preprint arXiv …,2017 - arxiv.orgGoogle Scholar
  8. 8.
    Singh, D., Prasad, S., Srivastava, S.: Implementation of artificial intelligence cognitive neuroscience neuron cell using adaptive velocity threshold particle swarm optimization (AVT-PSO) on FPGA. In: 2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, pp. 548–552 (2017).
  9. 9.
    Hao, X., Liu, Y., Li, X., Zhang, Y.: A two-factors and multi-orders self-adaptive fuzzy time series model based on fuzzy logical relationships trees and particle swarm optimization. In: 2017 3rd IEEE International Conference on Computer and Communications (ICCC), Chengdu, pp. 2537–2543 (2017).
  10. 10.
    Teo, K.T.K., Lim, P.Y., Chua, B., Goh, H.H., Tan, M.K.: Particle swarm optimization based maximum power point tracking for partially shaded photovoltaic arrays. Int. J. Simul. Syst. Sci. Technol. 17(34), 20.1–20.7, 7p (2016)Google Scholar
  11. 11.
    Cui, X., Potok, T.E., Palathingal, P.: Document Clustering using Particle Swarm Optimization. 0-7803-8916- 6/05/$20.00 ©2005IEEEGoogle Scholar
  12. 12.
    Kennedy, J., Eberhart, R.C., Particle Swarm Optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  13. 13.
    Hyma, J., Jhansi, Y., Anuradha, S.: A new hybridized approach of PSO & GA for document clustering. Int. J. Eng. Sci. Technol. 2(5), 1221–1226 (2010). Anderberg, M.R.: Cluster Analysis for Applications. Academic Press, Inc., New York, NY (1973)Google Scholar
  14. 14.
    Alam, S., Dobbie, G., Koh, Y.S., Riddle, P., Rehman, S.U.: Research on particle swarm optimization based clustering. Swarm Evol. Comput. 17, 1–13 (2014). Journal homepage:
  15. 15.
    Asai, K., Sugeno, M., Terano, T.: Applied Fuzzy Systems. Academic Press, New York (1994)Google Scholar
  16. 16.
    Zadeh, L.: Fuzzy sets. Inf. Control, pp. 338–353 (1965)MathSciNetCrossRefGoogle Scholar
  17. 17.

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Rajesh Eswarawaka
    • 1
  • C Subash Chandra
    • 2
  • Vadali Srinivas
    • 3
    Email author
  • Kanumuri Viswas
    • 4
  1. 1.Jawaharlal Nehru Technological University (JNTUK)KakinadaIndia
  2. 2.KIETKakinadaIndia
  3. 3.JNTUKKakinadaIndia
  4. 4.IIIT HyderabadHyderabadIndia

Personalised recommendations