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Computational Intelligence and Neuroscience
Volume 2016, Article ID 2565809, 10 pages
http://dx.doi.org/10.1155/2016/2565809
Research Article

A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems

1Department of Control Science and Engineering, Tongji University, Shanghai 201804, China
2BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824, USA

Received 23 January 2016; Revised 15 April 2016; Accepted 3 May 2016

Academic Editor: Jens Christian Claussen

Copyright © 2016 Leilei Cao 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. I. Fister Jr., D. Fister, and X. S. Yang, “A hybrid bat algorithm,” Elektrotehniški Vestnik, vol. 80, no. 1-2, pp. 1–7, 2013. View at Google Scholar
  2. W. L. Goffe, G. D. Ferrier, and J. Rogers, “Global optimization of statistical functions with simulated annealing,” Journal of Econometrics, vol. 60, no. 1-2, pp. 65–99, 1994. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  3. D. Whitley, “A genetic algorithm tutorial,” Statistics and Computing, vol. 4, no. 2, pp. 65–85, 1994. View at Publisher · View at Google Scholar · View at Scopus
  4. 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, Nagoya, Japan, October 1995. View at Scopus
  5. M. Dorigo, M. Birattari, and T. Stützle, “Ant colony optimization,” IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28–39, 2006. View at Google Scholar
  6. 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
  7. X. S. Yang, “Firefly algorithm,” in Engineering Optimization, pp. 221–230, John Wiley & Sons, New York, NY, USA, 2010. View at Google Scholar
  8. X.-S. Yang, “Firefly algorithm, stochastic test functions and design optimization,” International Journal of Bio-Inspired Computation, vol. 2, no. 2, pp. 78–84, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. X. S. Yang, “A new metaheuristic bat-inspired algorithm,” in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), vol. 284 of Studies in Computational Intelligence, pp. 65–74, Springer, Berlin, Germany, 2010. View at Publisher · View at Google Scholar
  10. B. Y. Qu, P. N. Suganthan, and J. J. Liang, “Differential evolution with neighborhood mutation for multimodal optimization,” IEEE Transactions on Evolutionary Computation, vol. 16, no. 5, pp. 601–614, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. G. Wang and L. Guo, “A novel hybrid bat algorithm with harmony search for global numerical optimization,” Journal of Applied Mathematics, vol. 2013, Article ID 696491, 21 pages, 2013. View at Google Scholar · View at MathSciNet
  12. H. Xu, C. Caramanis, and S. Mannor, “Sparse algorithms are not stable: a no-free-lunch theorem,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 1, pp. 187–193, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Sanner and C. Boutilier, “Approximate linear programming for first-order MDPs,” in Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence (UAI '05), pp. 509–517, Edinburgh, UK, July 2005.
  14. S. Yazdani, H. Nezamabadi-Pour, and S. Kamyab, “A gravitational search algorithm for multimodal optimization,” Swarm and Evolutionary Computation, vol. 14, pp. 1–14, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. G. Obregon-Henao, B. Babadi, C. Lamus et al., “A fast iterative greedy algorithm for MEG source localization,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '12), pp. 6748–6751, San Diego, Calif, USA, September 2012. View at Publisher · View at Google Scholar
  16. R. S. Parpinelli and H. S. Lopes, “New inspirations in swarm intelligence: a survey,” International Journal of Bio-Inspired Computation, vol. 3, no. 1, pp. 1–16, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Kennedy, “Particle swarm optimization,” in Encyclopedia of Machine Learning, pp. 760–766, Springer, 2010. View at Publisher · View at Google Scholar
  18. R. C. Eberhart and Y. Shi, “Particle swarm optimization: developments, applications and resources,” in Proceedings of the Congress on Evolutionary Computation, pp. 81–86, IEEE, Seoul, South Korea, May 2001. View at Scopus
  19. X.-S. Yang and A. Hossein Gandomi, “Bat algorithm: a novel approach for global engineering optimization,” Engineering Computations, vol. 29, no. 5, pp. 464–483, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. P.-W. Tsai, J.-S. Pan, B.-Y. Liao, M.-J. Tsai, and V. Istanda, “Bat algorithm inspired algorithm for solving numerical optimization problems,” Applied Mechanics and Materials, vol. 148-149, pp. 134–137, 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. X.-S. Yang and X. He, “Bat algorithm: literature review and applications,” International Journal of Bio-Inspired Computation, vol. 5, no. 3, pp. 141–149, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. I. Fister, S. Fong, J. Brest, and I. Fister, “A novel hybrid self-adaptive bat algorithm,” The Scientific World Journal, vol. 2014, Article ID 709738, 12 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  23. H. Liu, F. Gu, and X. Li, “A fast evolutionary algorithm with search preference,” International Journal of Computational Science and Engineering, vol. 3/4, no. 3-4, pp. 197–212, 2012. View at Google Scholar
  24. Y. Tan and Y. Zhu, “Fireworks algorithm for optimization,” in Advances in Swarm Intelligence, pp. 355–364, Springer, Berlin, Germany, 2010. View at Google Scholar
  25. X. S. Yang, “Flower pollination algorithm for global optimization,” in Unconventional Computation and Natural Computation, pp. 240–249, Springer, Berlin, Germany, 2012. View at Google Scholar
  26. D. Whitley, S. Rana, J. Dzubera, and K. E. Mathias, “Evaluating evolutionary algorithms,” Artificial Intelligence, vol. 85, no. 1-2, pp. 245–276, 1996. View at Publisher · View at Google Scholar · View at Scopus