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Mathematical Problems in Engineering
Volume 2016, Article ID 9791060, 10 pages
http://dx.doi.org/10.1155/2016/9791060
Research Article

Particle Swarm and Bacterial Foraging Inspired Hybrid Artificial Bee Colony Algorithm for Numerical Function Optimization

1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things, Jiangnan University, Wuxi, Jiangsu 214122, China
2Laboratory of Computational Geodynamics, University of Chinese Academy of Sciences, Beijing 100049, China
3Freshwater Fisheries Research Center, Chinese Academy of Fishery Science, Wuxi, Jiangsu 214081, China

Received 4 November 2015; Revised 4 January 2016; Accepted 4 January 2016

Academic Editor: Matjaz Perc

Copyright © 2016 Li 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. 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
  2. M. Dorigo, V. Maniezzo, and A. Colorni, “Ant system: optimization by a colony of cooperating agents,” IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, vol. 26, no. 1, pp. 29–41, 1996. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, IEEE, Perth, Australia, December 1995. View at Scopus
  4. X.-L. Li, Z.-J. Shao, and J.-X. Qian, “Optimizing method based on autonomous animats: fish-swarm Algorithm,” System Engineering Theory and Practice, vol. 22, no. 11, pp. 32–38, 2002. View at Google Scholar · View at Scopus
  5. X.-S. Yang, “Firefly algorithms for multimodal optimization,” in Stochastic Algorithms: Foundations and Applications, vol. 5792, pp. 169–178, Springer, Berlin, Germany, 2009. View at Publisher · View at Google Scholar · View at MathSciNet
  6. I. Fister, M. Perc, S. M. Kamal, and I. Fister, “A review of chaos-based firefly algorithms: perspectives and research challenges,” Applied Mathematics and Computation, vol. 252, pp. 155–165, 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. D. Karaboga, “An idea based on honey bee swarm for numerical optimization (vol. 200),” Tech. Rep. tr06, Computer Engineering Department, Engineering Faculty, Erciyes University, Kayseri, Turkey, 2005. View at Google Scholar
  8. A. A. El-Fergany and A. Y. Abdelaziz, “Capacitor placement for net saving maximization and system stability enhancement in distribution networks using artificial bee colony-based approach,” International Journal of Electrical Power & Energy Systems, vol. 54, pp. 235–243, 2014. View at Publisher · View at Google Scholar · View at Scopus
  9. O. Bulut and M. F. Tasgetiren, “An artificial bee colony algorithm for the economic lot scheduling problem,” International Journal of Production Research, vol. 52, no. 4, pp. 1150–1170, 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Ma, J. Liang, M. Guo, Y. Fan, and Y. Yin, “SAR image segmentation based on artificial bee colony algorithm,” Applied Soft Computing Journal, vol. 11, no. 8, pp. 5205–5214, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. A. Draa and A. Bouaziz, “An artificial bee colony algorithm for image contrast enhancement,” Swarm and Evolutionary Computation, vol. 16, pp. 69–84, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Singh, “An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem,” Applied Soft Computing Journal, vol. 9, no. 2, pp. 625–631, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. J. C. Bansal, H. Sharma, K. V. Arya, K. Deep, and M. Pant, “Self-adaptive artificial bee colony,” Optimization, vol. 63, no. 10, pp. 1513–1532, 2014. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  14. 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 Scopus
  15. F. Kang, J. Li, and Z. Ma, “Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions,” Information Sciences, vol. 181, no. 16, pp. 3508–3531, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  16. B. Alatas, “Chaotic bee colony algorithms for global numerical optimization,” Expert Systems with Applications, vol. 37, no. 8, pp. 5682–5687, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. 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
  18. X. Li and M. Yin, “Self-adaptive constrained artificial bee colony for constrained numerical optimization,” Neural Computing and Applications, vol. 24, no. 3-4, pp. 723–734, 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. K. M. Passino, “Biomimicry of bacterial foraging for distributed optimization and control,” IEEE Control Systems Magazine, vol. 22, no. 3, pp. 52–67, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Kennedy, “Particle swarm optimization,” in Encyclopedia of Machine Learning, pp. 760–766, Springer, New York, NY, USA, 2010. View at Google Scholar
  21. R. C. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proceedings of the 6th International Symposium on Micro Machine and Human Science, vol. 1, pp. 39–43, IEEE, Nagoya, Japan, October 1995. View at Scopus
  22. Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in Proceedings of the IEEE International Conference on Evolutionary Computation Proceedings and the IEEE World Congress on Computational Intelligence, pp. 69–73, IEEE, Anchorage, Alaska, USA, May 1998. View at Publisher · View at Google Scholar
  23. Y. Shi and R. C. Eberhart, “Empirical study of particle swarm optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '99), vol. 3, Washington, DC, USA, July 1999. View at Publisher · View at Google Scholar
  24. J. J. Liang, B. Y. Qu, and P. N. Suganthan, “Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization,” Tech. Rep., Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China; Nanyang Technological University, Singapore, 2013. View at Google Scholar
  25. A. Banharnsakun, T. Achalakul, and B. Sirinaovakul, “The best-so-far selection in artificial bee colony algorithm,” Applied Soft Computing Journal, vol. 11, no. 2, pp. 2888–2901, 2011. View at Publisher · View at Google Scholar · View at Scopus
  26. 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
  27. L. Dos Santos Coelho and P. Alotto, “Gaussian artificial bee colony algorithm approach applied to Loney's solenoid benchmark problem,” IEEE Transactions on Magnetics, vol. 47, no. 5, pp. 1326–1329, 2011. View at Publisher · View at Google Scholar · View at Scopus
  28. W. Gao and S. Liu, “Improved artificial bee colony algorithm for global optimization,” Information Processing Letters, vol. 111, no. 17, pp. 871–882, 2011. View at Publisher · View at Google Scholar · View at Scopus
  29. I. Fister, I. Fister Jr., J. Brest, and V. Žumer, “Memetic artificial bee colony algorithm for large-scale global optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '12), pp. 1–8, Brisbane, Australia, June 2012. View at Publisher · View at Google Scholar · View at Scopus