Advertisement

Journal of Optimization Theory and Applications

, Volume 155, Issue 3, pp 1095–1104 | Cite as

Parallel Implementation of Synchronous Type Artificial Bee Colony Algorithm for Global Optimization

  • Alper Basturk
  • Rustu Akay
Article

Abstract

Evolutionary algorithms often need huge running times when solving large-scale optimization problems. One of the solutions for this issue is to introduce parallelization into the algorithm. To benefit from this approach for the artificial bee colony optimization algorithm, we present a new synchronous and parallel version of the algorithm. Performances of the proposed version and the original asynchronous algorithm are compared in terms of efficiency and speedup. Algorithms are competed to solve 20 large-scale global optimization problems. Comparative results show that the proposed parallel algorithm is still efficient as asynchronous version while it requires much less time to solve complex and large problems.

Keywords

Parallel algorithms Parallel computing Artificial bee colony optimization algorithm Global optimization 

Notes

Acknowledgements

The numerical calculations reported in this paper are performed at TUBITAK ULAKBIM, High Performance and Grid Computing Center (TR-Grid e-Infrastructure).

This study is a part of the research project under the contract number FBA-11-3520 and the authors would like to thank Erciyes University, Scientific Research Projects Unit.

References

  1. 1.
    Schwefel, H.P.: Numerical Optimization of Computer Models. Wiley, Chichester (1981) zbMATHGoogle Scholar
  2. 2.
    Alba, E., Troya, J.M.: A survey of parallel distributed genetic algorithms. Complexity 4(4), 31–52 (1999) MathSciNetCrossRefGoogle Scholar
  3. 3.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Tech. Report No. TR06, Erciyes University (2005) Google Scholar
  4. 4.
    Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39, 459–471 (2007) MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Luo, R., Pan, S.T., Tsai, P.W., Pan, J.S.: Parallelized artificial bee colony with ripple-communication strategy. In: Fourth International Conference on Genetic and Evolutionary Computing, pp. 350–353 (2010) Google Scholar
  6. 6.
    Subotic, M., Tuba, M., Stanarevic, N.: Parallelization of the artificial bee colony (ABC) algorithm. In: Proceedings of the 11th WSEAS International Conference on Nural Networks and 11th WSEAS International Conference on Evolutionary Computing and 11th WSEAS International Conference on Fuzzy systems, pp. 191–196 (2010) Google Scholar
  7. 7.
    Subotic, M., Tuba, M., Stanarevic, N.: Different approaches in parallelization of the artificial bee colony algorithm. Int. J. Math. Models Methods Appl. Sci. 5(4), 755–762 (2011) Google Scholar
  8. 8.
    Benitez, C.M.V., Lopes, H.S.: Parallel artificial bee colony algorithm approaches for protein structure prediction using the 3DHP-SC model. Intell. Distrib. Comput. IV, 255–264 (2010) CrossRefGoogle Scholar
  9. 9.
    Parpinelli, R.S., Benitez, C.M.V., Lopes, H.S.: Parallel approaches for the artificial bee colony algorithm. In: Panigrahi, B.K., Shi, Y., Lim, M.H., Hiot, L.M., Ong, Y.S. (eds.) Handbook of Swarm Intelligence, Adaptation, Learning, and Optimization, vol. 8, pp. 329–345. Springer, Berlin (2010) Google Scholar
  10. 10.
    Gabriel, E., Fagg, G.E., Bosilca, G., Angskun, T., Dongarra, J.J., Squyres, J.M., Sahay, V., Kambadur, P., Barrett, B., Lumsdaine, A., Castain, R.H., Daniel, D.J., Graham, R.L., Woodall, T.S.: Open MPI: goals, concept, and design of a next generation MPI implementation. In: 11th European PVM/MPI Users’ Group Meeting Proceedings, pp. 97–104 (2004) Google Scholar
  11. 11.
    Antoni, G.A., Cabianca, D., Vaccari, M., Benini, M., Casablanca, F.: Linearity of client/server systems. Bull. Eur. Assoc. Theor. Comput. Sci. 57, 201–214 (1995) zbMATHGoogle Scholar
  12. 12.
    Tang, K., Li, X., Suganthan, P.N., Weise, T.: Benchmark functions for the CEC’2010 special session and competition on large-scale global optimization. Tech. report at http://nical.ustc.edu.cn/cec10ss.php, Nature Inspired Computation and Applications Laboratory, USTC, China (2009)

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Computer EngineeringErciyes UniversityKayseriTurkey
  2. 2.Graduate School of Natural & Applied SciencesErciyes UniversityKayseriTurkey

Personalised recommendations