Classification via Nearest Prototype Classifier Utilizing Artificial Bee Colony on CUDA

  • Jan Janousešek
  • Petr Gajdoš
  • Michal Radecký
  • Václav Snášel
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 299)


Artificial bee colony is a metaheuristic optimization algorithm based on the behaviour of honey bee swarm. These bees work largely independently of other bees, making the algorithm suitable for parallel implementation. Within this paper, we introduce the algorithm itself and its subsequent parallelization utilizing the CUDA platform. The runtime speedup is demonstrated on several commonly used test functions for optimization. The algorithm is subsequently applied to the problem of classifying real data.


parallel algorithm artificial bee colony CUDA nearest prototype classifier classification 


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  1. 1.
    Dorigo, M., Di Caro, G.: Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999, vol. 2, pp. 1470–1477 (1999)Google Scholar
  2. 2.
    Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intelligence 1(1), 33–57 (2007)CrossRefGoogle Scholar
  3. 3.
    Aickelin, U., Dasgupta, D., Gu, F.: Artificial immune systems. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies, pp. 187–211. Springer US (2014)Google Scholar
  4. 4.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report, Computer Engineering Department, Erciyes University, Turkey (2005)Google Scholar
  5. 5.
    Karaboga, D., Basturk, B.: Artificial bee colony (abc) optimization algorithm for solving constrained optimization problems. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds.) IFSA 2007. LNCS (LNAI), vol. 4529, pp. 789–798. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Karaboga, D., Akay, B., Ozturk, C.: Artificial bee colony (abc) optimization algorithm for training feed-forward neural networks. In: Torra, V., Narukawa, Y., Yoshida, Y. (eds.) MDAI 2007. LNCS (LNAI), vol. 4617, pp. 318–329. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Zhang, C., Ouyang, D., Ning, J.: An artificial bee colony approach for clustering. Expert Syst. Appl. 37(7), 4761–4767 (2010)CrossRefGoogle Scholar
  8. 8.
    Hadidi, A., Azad, S.K., Azad, S.K.: Structural optimization using artificial bee colony algorithm. In: 2nd International Conference on Engineering Optimization, Lisbon, Portugal, September 6-9 (2010)Google Scholar
  9. 9.
    Pan, Q.K., Tasgetiren, M.F., Suganthan, P., Chua, T.: A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Information Sciences 181(12), 2455–2468 (2011)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Zhang, Y., Wu, L., Wang, S.: Magnetic resonance brain image classification by an improved artificial bee colony algorithm. Progress in Electromagnetics Research 116, 65–79 (2011)Google Scholar
  11. 11.
    Zhang, Y., Wu, L.: Artificial bee colony for two dimensional protein folding. Advances in Electrical Engineering Systems 1(1), 19–23 (2012)Google Scholar
  12. 12.
    TSai, P.W., Pan, J.S., Liao, B.Y., Chu, S.C.: Enhanced artificial bee colony optimization. International Journal of Innovative Computing, Information and Control 5(12), 5081–5092 (2009)Google Scholar
  13. 13.
    Brajevic, I., Tuba, M.: An upgraded artificial bee colony (abc) algorithm for constrained optimization problems. Journal of Intelligent Manufacturing 24(4), 729–740 (2013)CrossRefGoogle Scholar
  14. 14.
    Kang, F., Li, J., Li, H., Ma, Z., Xu, Q.: An improved artificial bee colony algorithm. In: 2010 2nd International Workshop on Intelligent Systems and Applications (ISA), pp. 1–4 (2010)Google Scholar
  15. 15.
    Penev, K., Littlefair, G.: Free search a comparative analysis. Information Sciences 172(1-2), 173–193 (2005)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Subotic, M., Tuba, M., Stanarevic, N.: Different approaches in parallelization of the artificial bee colony algorithm. International Journal of Mathematical Models and Methods in Applied Sciences 5(4), 755–762 (2011)Google Scholar
  17. 17.
    Hong, Y.S., Ji, Z.Z., Liu, C.L.: Research of parallel artificial bee colony algorithm based on mpi. Applied Mechanics and Materials 380, 1430–1433 (2013)CrossRefGoogle Scholar
  18. 18.
    Luo, G.H., Huang, S.K., Chang, Y.S., Yuan, S.M.: A parallel bees algorithm implementation on GPU. Journal of Systems Architecture (2013)Google Scholar
  19. 19.
    Celik, M., Karaboga, D., Koylu, F.: Artificial bee colony data miner (abc-miner). In: 2011 International Symposium on Innovations in Intelligent Systems and Applications (INISTA), pp. 96–100 (2011)Google Scholar
  20. 20.
    Falco, I.D., Cioppa, A.D., Tarantino, E.: Facing classification problems with particle swarm optimization. Applied Soft Computing 7(3), 652–658 (2007)CrossRefGoogle Scholar
  21. 21.
    Bache, K., Lichman, M.: UCI machine learning repository (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jan Janousešek
    • 1
  • Petr Gajdoš
    • 1
  • Michal Radecký
    • 1
  • Václav Snášel
    • 1
  1. 1.Department of Computer Science, FEECSVŠB - Technical University of OstravaOstrava-PorubaCzech Republic

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