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Computational Intelligence and Neuroscience
Volume 2016, Article ID 9820294, 13 pages
http://dx.doi.org/10.1155/2016/9820294
Research Article

A New Modified Artificial Bee Colony Algorithm with Exponential Function Adaptive Steps

1Department of Mathematics, Sichuan University of Science & Engineering, Zigong, Sichuan 643000, China
2Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things, Zigong, Sichuan 643000, China
3School of Automation and Electronic Information, Sichuan University of Science & Engineering, Zigong, Sichuan 643000, China

Received 26 July 2015; Revised 10 October 2015; Accepted 19 October 2015

Academic Editor: Yufeng Zheng

Copyright © 2016 Wei Mao et al. 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. W. L. Xiang, S. F. Ma, and M. Q. An, “Habcde: a hybrid evolutionary algorithm based on artificial bee colony algorithm and differential evolution,” Applied Mathematics and Computation, vol. 238, pp. 370–386, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  2. H.-C. Tsai, “Integrating the artificial bee colony and bees algorithm to face constrained optimization problems,” Information Sciences, vol. 258, pp. 80–93, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  3. M. F. Tasgetiren, Q.-K. Pan, P. N. Suganthan, and A. Oner, “A discrete artificial bee colony algorithm for the no-idle permutation flowshop scheduling problem with the total tardiness criterion,” Applied Mathematical Modelling, vol. 37, no. 10-11, pp. 6758–6779, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  4. K. S. Tang, K. F. Man, S. Kwong, and Q. He, “Genetic algorithms and their applications,” IEEE Signal Processing Magazine, vol. 13, no. 6, pp. 22–37, 1996. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Kennedy, “Particle swarm optimization,” in Encyclopedia of Machine Learning, C. Sammut and G. I. Webb, Eds., pp. 760–766, Springer US, 2010. View at Google Scholar
  6. M. Dorigo and L. M. Gambardella, “Ant colony system: a cooperative learning approach to the traveling salesman problem,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 53–66, 1997. View at Publisher · View at Google Scholar · View at Scopus
  7. D. Simon, “Biogeography-based optimization,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 6, pp. 702–713, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Zhang and S. Liu, “A novel artificial bee colony algorithm for function optimization,” Mathematical Problems in Engineering, vol. 2015, Article ID 129271, 10 pages, 2015. View at Publisher · View at Google Scholar
  9. D. Karaboga, “An idea based on honey bee swarm for numerical optimization,” Technical Report tr06, Computer Engineering Department, Engineering Faculty, Erciyes University, Kayseri, Turkey, 2005. View at Google Scholar
  10. M. El-Abd, “Performance assessment of foraging algorithms vs. evolutionary algorithms,” Information Sciences, vol. 182, no. 1, pp. 243–263, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  11. T. Y. Lim, M. A. Al-Betar, and A. T. Khader, “Adaptive pair bonds in genetic algorithm: an application to real-parameter optimization,” Applied Mathematics and Computation, vol. 252, pp. 503–519, 2015. View at Publisher · View at Google Scholar · View at Scopus
  12. Y. Xiang, Y. Peng, Y. Zhong, Z. Chen, X. Lu, and X. Zhong, “A particle swarm inspired multi-elitist artificial bee colony algorithm for real-parameter optimization,” Computational Optimization and Applications, vol. 57, no. 2, pp. 493–516, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. D. Karaboga, B. Gorkemli, C. Ozturk, and N. Karaboga, “A comprehensive survey: artificial bee colony (ABC) algorithm and applications,” Artificial Intelligence Review, vol. 42, no. 1, pp. 21–57, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. J. C. Bansal, H. Sharma, and S. S. Jadon, “Artificial bee colony algorithm: a survey,” International Journal of Advanced Intelligence Paradigms, vol. 5, no. 1-2, pp. 123–159, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. A. L. Bolaji, A. T. Khader, M. A. Al-Betar, and M. A. Awadallah, “Artificial bee colony algorithm, its variants and applications: a survey,” Journal of Theoretical and Applied Information Technology, vol. 47, no. 2, pp. 434–459, 2013. View at Google Scholar · View at Scopus
  16. H. Habbi, Y. Boudouaoui, D. Karaboga, and C. Ozturk, “Self-generated fuzzy systems design using artificial bee colony optimization,” Information Sciences, vol. 295, pp. 145–159, 2015. View at Publisher · View at Google Scholar · View at MathSciNet
  17. L. N. Vitorino, S. F. Ribeiro, and C. J. A. Bastos-Filho, “A mechanism based on artificial bee colony to generate diversity in particle swarm optimization,” Neurocomputing, vol. 148, pp. 39–45, 2015. View at Publisher · View at Google Scholar · View at Scopus
  18. M. S. Kiran and O. Findik, “A directed artificial bee colony algorithm,” Applied Soft Computing Journal, vol. 26, pp. 454–462, 2015. View at Publisher · View at Google Scholar · View at Scopus
  19. D. Karaboga and B. Gorkemli, “A quick artificial bee colony (qABC) algorithm and its performance on optimization problems,” Applied Soft Computing Journal, vol. 23, pp. 227–238, 2014. View at Publisher · View at Google Scholar · View at Scopus
  20. N. Imanian, M. E. Shiri, and P. Moradi, “Velocity based artificial bee colony algorithm for high dimensional continuous optimization problems,” Engineering Applications of Artificial Intelligence, vol. 36, pp. 148–163, 2014. View at Publisher · View at Google Scholar · View at Scopus
  21. H. Wang, Z. Wu, S. Rahnamayan, H. Sun, Y. Liu, and J.-s. Pan, “Multi-strategy ensemble artificial bee colony algorithm,” Information Sciences, vol. 279, pp. 587–603, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  22. W.-F. Gao, S.-Y. Liu, and L.-L. Huang, “Enhancing artificial bee colony algorithm using more information-based search equations,” Information Sciences, vol. 270, pp. 112–133, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  23. S. Das, S. Biswas, and S. Kundu, “Synergizing fitness learning with proximity-based food source selection in artificial bee colony algorithm for numerical optimization,” Applied Soft Computing, vol. 13, no. 12, pp. 4676–4694, 2013. View at Publisher · View at Google Scholar · View at Scopus
  24. M. X. Zang, X. Ma, and Y. M. Duan, “Improved artificial bee colony algorithm,” Journal of Xidian University, vol. 42, no. 2, pp. 65–70, 2015. View at Google Scholar
  25. C. Zhang, J. Zheng, and Y. Zhou, “Two modified artificial bee colony algorithms inspired by grenade explosion method,” Neurocomputing, vol. 151, no. 3, pp. 1198–1207, 2015. View at Publisher · View at Google Scholar · View at Scopus
  26. M. Mernik, S.-H. Liu, D. Karaboga, and M. Črepinšek, “On clarifying misconceptions when comparing variants of the artificial bee colony algorithm by offering a new implementation,” Information Sciences, vol. 291, pp. 115–127, 2015. View at Publisher · View at Google Scholar · View at MathSciNet
  27. M. S. Kiran, H. Hakli, M. Gunduz, and H. Uguz, “Artificial bee colony algorithm with variable search strategy for continuous optimization,” Information Sciences, vol. 300, pp. 140–157, 2015. View at Publisher · View at Google Scholar · View at MathSciNet
  28. T. G. Chen and R. B. Xiao, “Modeling design iteration in product design and development and its solution by a novel artificial bee colony algorithm,” Computational Intelligence and Neuroscience, vol. 2014, Article ID 240828, 13 pages, 2014. View at Publisher · View at Google Scholar
  29. B. Li, “Research on WNN modeling for gold price forecasting based on improved artificial bee colony algorithm,” Computational Intelligence and Neuroscience, vol. 2014, Article ID 270658, 10 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  30. S. Wei and N. Z. Lu, “An improvement for artificial bee algorithm,” Modern Computer, vol. 6, no. 17, pp. 25–29, 2014. View at Google Scholar
  31. L. Li, F. M. Yao, L. J. Tan et al., “A novel DE-ABC-based hybrid algorithm for global optimization,” in Proceedings of the 7th International Conference on Intelligent Computing (ICIC '11), Zhengzhou, China, August 2011, D. S. Huang, Y. Gan, P. Premaratne, and K. Han, Eds., Lecture Notes in Computer Science, pp. 558–565, Springer, Berlin, Germany, 2012. View at Publisher · View at Google Scholar
  32. S.-M. Chen, A. Sarosh, and Y.-F. Dong, “Simulated annealing based artificial bee colony algorithm for global numerical optimization,” Applied Mathematics and Computation, vol. 219, no. 8, pp. 3575–3589, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  33. S. C. Satapathy and A. Naik, “Modified teaching-learning-based optimization algorithm for global numerical optimization—a comparative study,” Swarm and Evolutionary Computation, vol. 16, pp. 28–37, 2014. View at Publisher · View at Google Scholar · View at Scopus
  34. Q. Xu, L. Wang, N. Wang, X. Hei, and L. Zhao, “A review of opposition-based learning from 2005 to 2012,” Engineering Applications of Artificial Intelligence, vol. 29, pp. 1–12, 2014. View at Publisher · View at Google Scholar · View at Scopus
  35. K. Penev, “Free search—comparative analysis 100,” International Journal of Metaheuristics, vol. 3, no. 2, pp. 118–132, 2014. View at Publisher · View at Google Scholar