Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2013 (2013), Article ID 370172, 11 pages
http://dx.doi.org/10.1155/2013/370172
Research Article

An Improved Marriage in Honey Bees Optimization Algorithm for Single Objective Unconstrained Optimization

1Department of Computer Programming, Karamanoglu Mehmetbey University, Karaman, Turkey
2Computer Engineering Department, Selcuk University, Konya, Turkey

Received 5 May 2013; Accepted 11 June 2013

Academic Editors: P. Agarwal, V. Bhatnagar, and Y. Zhang

Copyright © 2013 Yuksel Celik and Erkan Ulker. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. X.-S. Yang, “Levy flight,” in Nature-Inspired Metaheuristic Algorithms, pp. 14–17, Luniver Press, 2nd edition, 2010. View at Google Scholar
  2. D. Bunnag and M. Sun, “Genetic algorithm for constrained global optimization in continuous variables,” Applied Mathematics and Computation, vol. 171, no. 1, pp. 604–636, 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. H. E. Romeijn and R. L. Smith, “Simulated annealing for constrained global optimization,” Journal of Global Optimization, vol. 5, no. 2, pp. 101–126, 1994. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, December 1995. View at Scopus
  5. J. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, 1975.
  6. R. Storn and K. Price, “Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997. View at Google Scholar · View at Scopus
  7. O. B. Haddad, A. Afshar, and M. A. Mariño, “Honey-bees mating optimization (HBMO) algorithm: a new heuristic approach for water resources optimization,” Water Resources Management, vol. 20, no. 5, pp. 661–680, 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. H. A. Abbass, “MBO: Marriage in honey bees optimization a haplometrosis polygynous swarming approach,” in Proceedings of the Congress on Evolutionary Computation (CEC '01), pp. 207–214, May 2001. View at Scopus
  9. T. Thomas, Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford University Press, New York, NY, USA, 1996.
  10. C. Coello Coello and G. B. Lamont, Evolutionary Algorithms for Solving Multi-Objective Problems, Genetic Algorithms and Evolutionary Computation, Kluwer Academic Publishers, Boston, Mass, USA, 2nd edition, 2007.
  11. D. Karaboga and B. Basturk, “On the performance of artificial bee colony (ABC) algorithm,” Applied Soft Computing Journal, vol. 8, no. 1, pp. 687–697, 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. J. G. Digalakis and K. G. Margaritis, “On benchmarking functions for genetic algorithms,” International Journal of Computer Mathematics, vol. 77, no. 4, pp. 481–506, 2001. View at Google Scholar · View at Scopus
  13. M. M. Hassan, F. Karray, M. S. Kamel, and A. Ahmadi, “An integral approach for Geno-Simulated Annealing,” in Proceedings of the 10th International Conference on Hybrid Intelligent Systems (HIS '10), pp. 165–170, August 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. A. Chatterjee and P. Siarry, “Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization,” Computers and Operations Research, vol. 33, no. 3, pp. 859–871, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. R. S. Rahnamayan, H. R. Tizhoosh, and M. M. A. Salama, “Opposition-based differential evolution,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 1, pp. 64–79, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. H. A. Abbass and J. Teo, “A true annealing approach to the marriage in honey-bees optimization algorithm,” International Journal of Computational Intelligence and Aplications, vol. 3, pp. 199–208, 2003. View at Google Scholar
  17. H. S. Chang, “Converging marriage in honey-bees optimization and application to stochastic dynamic programming,” Journal of Global Optimization, vol. 35, no. 3, pp. 423–441, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. A. Afshar, O. Bozorg Haddad, M. A. Mariño, and B. J. Adams, “Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation,” Journal of the Franklin Institute, vol. 344, no. 5, pp. 452–462, 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. Y. Marinakis, M. Marinaki, and G. Dounias, “Honey bees mating optimization algorithm for the Euclidean traveling salesman problem,” Information Sciences, vol. 181, no. 20, pp. 4684–4698, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. C.-Y. Chiu and T. Kuo, “Applying honey-bee mating optimization and particle swarm optimization for clustering problems,” Journal of the Chinese Institute of Industrial Engineers, vol. 26, no. 5, pp. 426–431, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” Journal of Global Optimization, vol. 39, no. 3, pp. 459–471, 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. R. F. A. Moritzl and C. Brandesl, “Behavior genetics of honeybees (Apis mellifera L.),” in Neurobiology and Behavior of Honeybees, pp. 21–35, Springer, Berlin, Germany, 1987. View at Google Scholar
  23. R. F. A. Moritz and E. E. Southwick, Bees as Super-Organisms, Springer, Berlin, Germany, 1992.
  24. B. Bilgin, E. Özcan, and E. E. Korkmaz, “An experimental study on hyper-heuristics and exam scheduling,” in Practice and Theory of Automated Timetabling VI, vol. 3867 of Lecture Notes in Computer Science, pp. 394–412, Springer, 2007. View at Google Scholar
  25. A. Alcayde, R. Baños, C. Gil, F. G. Montoya, J. Moreno-Garcia, and J. Gómez, “Annealing-tabu PAES: a multi-objective hybrid meta-heuristic,” Optimization, vol. 60, no. 12, pp. 1473–1491, 2011. View at Publisher · View at Google Scholar · View at Scopus
  26. X.-S. Yang and S. Deb, “Multiobjective cuckoo search for design optimization,” Computers and Operations Research, vol. 40, no. 6, pp. 1616–1624, 2013. View at Publisher · View at Google Scholar · View at Scopus
  27. I. Pavlyukevich, “Lévy flights, non-local search and simulated annealing,” Journal of Computational Physics, vol. 226, no. 2, pp. 1830–1844, 2007. View at Publisher · View at Google Scholar · View at Scopus
  28. G. M. Viswanathan, F. Bartumeus, and S. V. Buldyrev, “Levy Flight random searches in biological phenomena,” Physica A, vol. 314, pp. 208–213, 2002. View at Google Scholar
  29. A. M. Reynolds, “Cooperative random Lévy flight searches and the flight patterns of honeybees,” Physics Letters A, vol. 354, no. 5-6, pp. 384–388, 2006. View at Publisher · View at Google Scholar · View at Scopus
  30. T. T. Tran, T. T. Nguyen, and H. L. Nguyen, “Global optimization using levy flight,” in Proceedings of the 3rd National Symposium on Research, Development and Application of Information and Communication Technology (ICT.rda '06), Hanoi, Vietnam, September 2004.
  31. S. He, “Training artificial neural networks using lévy group search optimizer,” Journal of Multiple-Valued Logic and Soft Computing, vol. 16, no. 6, pp. 527–545, 2010. View at Google Scholar · View at Scopus
  32. B. Akay, Nimerik Optimizasyon Problemlerinde Yapay Arı Kolonisi (Artifical Bee Colony, ABC) Algoritmasının Performans Analizi, Kayseri Üniversitesi, Fen Bilimleri Enstitüsü, Kayseri, Turkey, 2009.
  33. R. Akbari, A. Mohammadi, and K. Ziarati, “A novel bee swarm optimization algorithm for numerical function optimization,” Communications in Nonlinear Science and Numerical Simulation, vol. 15, no. 10, pp. 3142–3155, 2010. View at Publisher · View at Google Scholar · View at Scopus
  34. K. Sundareswaran and V. T. Sreedevi, “Development of novel optimization procedure based on honey bee foraging behavior,” in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC '08), pp. 1220–1225, October 2008. View at Publisher · View at Google Scholar · View at Scopus
  35. R. Venkata Rao and V. Patel, “An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems,” Scientia Iranica. In press.
  36. Y. Marinakis, M. Marinaki, and N. Matsatsinis, “A bumble bees mating optimization algorithm for global unconstrained optimization problems,” Studies in Computational Intelligence, vol. 284, pp. 305–318, 2010. View at Publisher · View at Google Scholar · View at Scopus