Clustering Using Improved Cuckoo Search Algorithm

  • Jie Zhao
  • Xiujuan Lei
  • Zhenqiang Wu
  • Ying Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8794)


Cuckoo search (CS) is one of the new swarm intelligence optimization algorithms inspired by the obligate brood parasitic behavior of cuckoo, which used the idea of Lévy flights. But the convergence and stability of the algorithm is not ideal due to the heavy-tail property of Lévy flights. Therefore an improved cuckoo search (ICS) algorithm for clustering is proposed, in which the movement and randomization of the cuckoo is modified. The simulation results of ICS clustering method on UCI benchmark data sets compared with other different clustering algorithms show that the new algorithm is feasible and efficient in data clustering, and the stability and convergence speed both get improved obviously.


Clustering cuckoo search Lévy flights swarm intelligence optimization algorithm 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Han, J.W., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers (2011)Google Scholar
  2. 2.
    Lei, X.J.: Swarm Intelligent Optimization Algorithms and their Applications. Science Press (2012)Google Scholar
  3. 3.
    Maulik, U., Bandyopadhyay, S.: Genetic Algorithm-based Clustering Technique. Pattern Recognition 33, 1455–1465 (2000)CrossRefGoogle Scholar
  4. 4.
    Kao, Y., Cheng, K.: An ACO-based clustering algorithm. In: 5th International Workshop on Ant Colony Optimization and Swarm Intelligence, pp. 340–347 (2006)Google Scholar
  5. 5.
    Van Der Merwe, D.W., Engelbrecht, A.P.: Data Clustering Using Particle Swarm Optimization. In: Congress on Evolutionary Computation (CEC 2003), pp. 215–220 (2003)Google Scholar
  6. 6.
    Zhang, Q., Lei, X.J., Huang, X., Zhang, A.D.: An Improved Projection Pursuit Clustering Model and its Application Based on Quantum-behaved PSO. In: 2010 Sixth International Conference on Natural Computation (ICNC 2010), vol. 5, pp. 2581–2585 (2010)Google Scholar
  7. 7.
    Zhang, C.S., Ouyang, D.T., Ning, J.X.: An Artificial Bee Colony Approach for Clustering. Expert Systems with Applications 37, 4761–4767 (2010)CrossRefGoogle Scholar
  8. 8.
    Lei, X.J., Tian, J.F., Ge, L., Zhang, A.D.: The Clustering Model and Algorithm of PPI Network Based on Propagating Mechanism of Artificial Bee Colony. Information Sciences 247, 21–39 (2013)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Lei, X.J., Wu, S., Ge, L., Zhang, A.D.: Clustering and Overlapping Modules Detection in PPI Network Based on IBFO. Proteomics 13, 278–290 (2013)CrossRefGoogle Scholar
  10. 10.
    Senthilnath, J., Omkar, S.N., Mani, V.: Clustering Using Firefly Algorithm: Performance study. Swarm and Evolutionary Computation 1, 164–171 (2011)CrossRefGoogle Scholar
  11. 11.
    Ghodrati, A., Lotfi, S.: A Hybrid CS/PSO Algorithm for Global Optimization. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ACIIDS 2012, Part III. LNCS, vol. 7198, pp. 89–98. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  12. 12.
    Basu, M., Chowdhury, A.: Cuckoo Search Algorithm for Economic Dispatch. Energy 60, 99–108 (2013)CrossRefGoogle Scholar
  13. 13.
    Saida, I.B., Nadjet, K., Omar, B.: A New Algorithm for Data Clustering Based on Cuckoo Search Optimization. Genetic and Evolutionary Computing 238, 55–64 (2014)CrossRefGoogle Scholar
  14. 14.
    Senthilnath, J., Das, V., Omkar, S.N., Mani, V.: Clustering Using Lévy Flight Cuckoo Search. In: Bansal, J.C., Singh, P., Deep, K., Pant, M., Nagar, A. (eds.) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications, (BIC-TA 2012). AISC, vol. 202, pp. 65–75. Springer, Heidelberg (2013)Google Scholar
  15. 15.
    Goel, S., Sharma, A., Bedi, P.: Cuckoo Search Clustering Algorithm: A Novel Strategy of Biomimicry. In: World Congress on Information and Communication Technologies, pp. 916–926 (2011)Google Scholar
  16. 16.
    Manikandan, P., Selvarajan, S.: Data Clustering Using Cuckoo Search Algorithm (CSA). In: Babu, B.V., Nagar, A., Deep, K., Pant, M., Bansal, J.C., Ray, K., Gupta, U. (eds.) Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012). AISC, vol. 236, pp. 1275–1283. Springer, Heidelberg (2014)Google Scholar
  17. 17.
    Bulatović, R.R., Đorđević, S.R., Đorđević, V.S.: Cuckoo Search Algorithm: A Metaheuristic Approach to Solving the Problem of Optimum Synthesis of a Six-bar Double Dwell Linkage. Mechanism and Machine Theory 61, 1–13 (2013)Google Scholar
  18. 18.
    Yang, X.S.: Nature-Inspired Metaheuristic Algorithms, 2nd edn. Luniver Press (2010)Google Scholar
  19. 19.
    Yang, X.S., Deb, S.: Cuckoo Search via Lévy Flights. In: World Congress on Nature & Biologically Inspired Computind, pp. 210–214. IEEE Publications, USA (2009)Google Scholar
  20. 20.
    Payne, R.B., Sorenson, M.D., Klitz, K.: The Cuckoos. Oxford University Press (2005)Google Scholar
  21. 21.
    Valian, E., Mohanna, S., Tavakoli, S.: Improved Cuckoo Search Algorithm for Feedforward Neural Network Training. International Journal of Artificial Intelligence & Applications 2, 36–43 (2011)CrossRefGoogle Scholar
  22. 22.
    Reynolds, A.M., Rhodes, C.J.: The Lévy Flight Paradigm: Random Search Patterns and Mechanisms. Concepts & Synthesis 90, 877–887 (2009)Google Scholar
  23. 23.
    Gandomi, A.H., Yang, X.S., Alavi, A.H.: Cuckoo Search Algorithm: a Metaheuristic Approach to Solve Structural Optimization Problems. Engineering with Computers 29, 17–35 (2013)CrossRefGoogle Scholar
  24. 24.
    Mantegna, R.N.: Fast, Accurate Algorithm for Numerical Simulation of Lévy Stable Stochastic Processes. Physical Review E 49, 4677–4689 (1994)CrossRefGoogle Scholar
  25. 25.
    UCI Machine Learning Repository,
  26. 26.
    Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An Efficient k-Means Clustering Algorithm. In: Analysis and Implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 881–892. IEEE Press, New York (2002)Google Scholar
  27. 27.
    Hassanzadeh, T., Meybodi, M.R.: A New Hybrid Approach for Data Clustering using Firefly Algorithm and K-means. In: CSI International Symposium on Artificial Intelligence and Signal Processing, pp. 7–11. IEEE Press, New York (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jie Zhao
    • 1
  • Xiujuan Lei
    • 1
    • 2
  • Zhenqiang Wu
    • 1
  • Ying Tan
    • 2
  1. 1.School of Computer ScienceShaanxi Normal UniversityXi’anChina
  2. 2.School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina

Personalised recommendations