A Hybrid Bumble Bees Mating Optimization - GRASP Algorithm for Clustering | SpringerLink
Skip to main content
Springer Nature Link
Log in

A Hybrid Bumble Bees Mating Optimization - GRASP Algorithm for Clustering

  • Conference paper
Hybrid Artificial Intelligence Systems (HAIS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5572))

Included in the following conference series:

Abstract

A new hybrid algorithm for clustering, which is based on the concepts of the Bumble Bees Mating Optimization (BBMO) and Greedy Randomized Adaptive Search Procedure (GRASP), is presented in this paper. The proposed algorithm is a two phase algorithm which combines a new algorithm called Bumble Bees Mating Optimization algorithm for the solution of the feature selection problem and a GRASP algorithm for the solution of the clustering problem. The performance of the algorithm is compared with other popular metaheuristic and nature inspired methods using datasets from the UCI Machine Learning Repository. The high performance of the proposed algorithm is achieved as the algorithm gives very good results and in some instances the percentage of the correct clustered samples is very high and is larger than 98%.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Abbass, H.A.: A monogenous MBO approach to satisfiability. In: Proceeding of the International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2001, Las Vegas, NV, USA (2001)

    Google Scholar 

  2. Dorigo, M., Stutzle, T.: Ant Colony Optimization. A Bradford Book. MIT Press, Cambridge (2004)

    MATH  Google Scholar 

  3. Feo, T.A., Resende, M.G.C.: Greedy randomized adaptive search procedure. Journal of Global Optimization 6, 109–133 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  4. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  5. Marinakis, Y., Marinaki, M., Matsatsinis, N.: A hybrid clustering algorithm based on Honey Bees Mating Optimization and Greedy Randomized Adaptive Search Procedure. In: Maniezzo, V., Battiti, R., Watson, J.-P. (eds.) LION 2008. LNCS, vol. 5313, pp. 138–152. Springer, Heidelberg (2008)

    Google Scholar 

  6. Rokach, L., Maimon, O.: Clustering methods. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook. Springer, New York (2005)

    Google Scholar 

  7. Xu, R., Wunsch II, D.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)

    Article  Google Scholar 

  8. http://www.bumblebee.org/

  9. http://www.everythingabout.net/articles/biology/animals/arthropods/insects/bees/bumble_bee/

  10. http://bumbleboosters.unl.edu/biology.shtml

  11. http://www.colostate.edu/Depts/Entomology/courses/en570/papers_1998/walter.htm

Download references

Author information

Authors and Affiliations

  1. Decision Support Systems Laboratory, Department of Production Engineering and Management, Technical University of Crete, 73100, Chania, Greece

    Yannis Marinakis & Nikolaos Matsatsinis

  2. Industrial Systems Control Laboratory, Department of Production Engineering and Management, Technical University of Crete, 73100, Chania, Greece

    Magdalene Marinaki

Authors
  1. Yannis Marinakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Magdalene Marinaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nikolaos Matsatsinis
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Grupo de Investigación GICAP, Área de Lenguajes Higher Polytechnic School, Universidad de Burgos, Burgos, Spain

    Emilio Corchado

  2. Department of Computer Science, University of Vermont, 33 Colchester Avenue, Burlington, VT, USA

    Xindong Wu

  3. Computer and Information Science, Helsinki University of Technology, P.O. Box 5400, 02015 HUT, Finland

    Erkki Oja

  4. Grupo de Investigación GICAP, Área de Lenguajes y Sistemas Informáticos, Departamento de Ingeniería Civil, Escuela Politécnica Superior, Universidad de Burgos, Campus Vena, Francisco de Vitoria, 09006, Burgos, Spain

    Álvaro Herrero  & Bruno Baruque  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Marinakis, Y., Marinaki, M., Matsatsinis, N. (2009). A Hybrid Bumble Bees Mating Optimization - GRASP Algorithm for Clustering. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_66

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-02319-4_66

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02318-7

  • Online ISBN: 978-3-642-02319-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation