Modeling an Artificial Bee Colony with Inspector for Clustering Tasks

  • Cosimo Birtolo
  • Giovanni Capasso
  • Davide Ronca
  • Gennaro Sorrentino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8600)

Abstract

Artificial Bee Colony (ABC) is a recent meta-heuristic approach. In this paper we face the problem of clustering by ABC and we model a further bee role in the colony, performed by inspector bee. This model conforms with real honey bee colony, indeed, in nature some bees among the foraging ones are called inspectors because they preserve the colony’s history and historical information related to food sources. We experiment inspector behavior in ABC and compare the solution to traditional clustering algorithm. Finally, the effect of colony size is investigated and experimental results are discussed.

Keywords

Artificial Bee Colony Soft Computing Clustering Inspector Data Mining 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recognition Letters 31(8), 651–666 (2010)CrossRefGoogle Scholar
  2. 2.
    Hruschka, E., Campello, R.J.G.B., Freitas, A., De Carvalho, A.C.P.L.F.: A survey of evolutionary algorithms for clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 39(2), 133–155 (2009)CrossRefGoogle Scholar
  3. 3.
    Karaboga, D., Ozturk, C.: A novel clustering approach: Artificial bee colony (ABC) algorithm. Applied Soft Computing 11(1), 652–657 (2011)CrossRefGoogle Scholar
  4. 4.
    Yan, X., Zhu, Y., Zou, W., Wang, L.: A new approach for data clustering using hybrid artificial bee colony algorithm. Neurocomput. 97, 241–250 (2012)CrossRefGoogle Scholar
  5. 5.
    Karaboga, D.: An idea based on Honey Bee Swarm for Numerical Optimization. Technical Report TR06, Erciyes University (October 2005)Google Scholar
  6. 6.
    Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing 8(1), 687–697 (2008)CrossRefGoogle Scholar
  7. 7.
    Abu-Mouti, F., El-Hawary, M.: Overview of artificial bee colony (ABC) algorithm and its applications. In: 2012 IEEE International Systems Conference (SysCon), pp. 1–6 (2012)Google Scholar
  8. 8.
    Biesmeijer, J.C., de Vries, H.: Exploration and exploitation of food sources by social insect colonies: a revision of the scout-recruit concept. Behavioral Ecology and Sociobiology 49(2-3), 89–99 (2001)CrossRefGoogle Scholar
  9. 9.
    Granovskiy, B., Latty, T., Duncan, M., Sumpter, D.J.T., Beekman, M.: How dancing honey bees keep track of changes: the role of inspector bees. Behavioral Ecology 23(3), 588–596 (2012)CrossRefGoogle Scholar
  10. 10.
    Bache, K., Lichman, M.: UCI machine learning repository (2013)Google Scholar
  11. 11.
    Akay, B., Karaboga, D.: Parameter tuning for the artificial bee colony algorithm. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS, vol. 5796, pp. 608–619. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Cosimo Birtolo
    • 1
  • Giovanni Capasso
    • 1
  • Davide Ronca
    • 1
  • Gennaro Sorrentino
    • 1
  1. 1.S - FSTI - R&D CenterPoste Italiane – Information TechnologyNaplesItaly

Personalised recommendations