Quantum-Inspired Bat Optimization Algorithm for Automatic Clustering of Grayscale Images

  • Alokananda DeyEmail author
  • Siddhartha Bhattacharyya
  • Sandip Dey
  • Jan Platos
  • Vaclav Snasel
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 922)


This paper introduces a quantum-inspired framework with the bat optimization algorithm for automatic clustering of image datasets. The aim of this work is to find out the optimal number of clusters from an image dataset on a run. A comparison has been produced between the quantum-inspired bat optimization algorithm and its classical counterpart. As a result, it is seen that the quantum-inspired version outperforms its classical counterpart. Computational experiments have been conducted on four Berkeley image datasets.


Automatic clustering Metaheuristic algorithm Quantum computing Bat optimization algorithm Statistical test \( (t - \text{test}) \) 


  1. 1.
    Jain A, Dubes R (1988) Algorithms for clustering data. Prentice HallGoogle Scholar
  2. 2.
    Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323CrossRefGoogle Scholar
  3. 3.
    Yadav J, Sharma M (2013) A review of k-mean algorithm. Int J Eng Trends Technology (IJETT) 4(7)Google Scholar
  4. 4.
    Narayanan A, Moore M (1996) Quantum-inspired genetic algorithms. In: Proceedings IEEE evolutionary computation, pp 61–66Google Scholar
  5. 5.
    Storn R, Price K (1195) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Technical Report TR-95-012, ICSIGoogle Scholar
  6. 6.
    Hui-liana F, Xian-lib L (2011) Discrete particle swarm optimization for TSP based on neighborhood. Appl Res Comput 2:030Google Scholar
  7. 7.
    Dey S, Bhattacharyya S, Maullik U (2018) Quantum-inspired automatic clustering technique using ant colony optimization algorithm. In Quantum-inspired intelligent systems for multimedia data analysis.
  8. 8.
    Yang X-S (2010) A new metaheuristic bat-inspired algorithm. Studies in Computational Intelligence 284:65–74zbMATHGoogle Scholar
  9. 9.
    Alihodzic A, Tuba M (2014) Improved hybridized bat algorithm for global numerical optimization. In: Proceedings of the 16th IEEE international conference on computer modelling and simulation (UKSim-AMSS ‘14), pp 57–62Google Scholar
  10. 10.
    Das Swagatam, Abraham Ajith, Konar Amit (2008) Automatic clustering using an improved differential evolution algorithm. IEEE Trans Syst Man Cybern Part A Syst Hum 38:218–237. Scholar
  11. 11.
    Mcmohan D (2008) Quantum computing explained. Wiley Inc, Hoboken, New JerseyGoogle Scholar
  12. 12.
    Dey S, Saha I, Bhattacharyya S, Maulik U (2014) Multi-level thresholding using quantum inspired meta-heuristics. Knowl-Based Syst 67:373–400CrossRefGoogle Scholar
  13. 13.
    Dey A, Dey S, Bhattacharyya S, Snasel V, Hassanien AE (2018) Simulated annealing based quantum inspired automatic clustering technique. In: Hassanien A, Tolba M, Elhoseny M, Mostafa M (eds) The international conference on advanced machine learning technologies and applications (AMLTA2018). AMLTA 2018. Advances in intelligent systems and computing, vol 723. Springer, ChamGoogle Scholar
  14. 14.
    Maulik U, Bandyopadhyay S (2002) Performance evaluation of some clustering algorithms and validity indices. IEEE PAMI 24:1650–1654CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Alokananda Dey
    • 1
    Email author
  • Siddhartha Bhattacharyya
    • 1
    • 2
  • Sandip Dey
    • 3
  • Jan Platos
    • 2
  • Vaclav Snasel
    • 2
  1. 1.Department of Computer ApplicationRCC Institute of Information TechnologyKolkataIndia
  2. 2.Faculty of Electrical Engineering and Computer ScienceVSB Technical University of OstravaOstravaCzech Republic
  3. 3.Department of Computer Science and EngineeringOmDayal Group of InstitutionsHowrahIndia

Personalised recommendations