Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2015, Article ID 583759, 11 pages
http://dx.doi.org/10.1155/2015/583759
Research Article

An Enhanced Differential Evolution with Elite Chaotic Local Search

1Institute of Medical Informatics and Engineering, School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China
2School of Literature and Law, Jiangxi University of Science and Technology, Ganzhou 341000, China
3School of Information Science and Technology, Jiujiang University, Jiujiang 332005, China
4State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China

Received 8 October 2014; Accepted 27 April 2015

Academic Editor: Rafik Aliyev

Copyright © 2015 Zhaolu Guo 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. 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 Publisher · View at Google Scholar · View at MathSciNet
  2. S. Das and P. N. Suganthan, “Differential evolution: a survey of the state-of-the-art,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 4–31, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Vesterstrøm and R. Thomsen, “A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems,” in Proceedings of the Congress on Evolutionary Computation (CEC '04), vol. 2, pp. 1980–1987, IEEE, June 2004. View at Publisher · View at Google Scholar · View at Scopus
  4. L. Wang and L.-P. Li, “Fixed-structure H controller synthesis based on differential evolution with level comparison,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 120–129, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. F. Martín, L. Moreno, M. L. Muñoz, and D. Blanco, “Initial population size estimation for a differential-evolution-based global localization filter,” International Journal of Robotics and Automation, vol. 29, no. 3, 2014. View at Publisher · View at Google Scholar
  6. F. Neri and V. Tirronen, “Recent advances in differential evolution: a survey and experimental analysis,” Artificial Intelligence Review, vol. 33, no. 1-2, pp. 61–106, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. A. K. Qin, V. L. Huang, and P. N. Suganthan, “Differential evolution algorithm with strategy adaptation for global numerical optimization,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 398–417, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. W. Gong, Z. Cai, C. X. Ling, and C. Li, “Enhanced differential evolution with adaptive strategies for numerical optimization,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 41, no. 2, pp. 397–413, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. H. Wang, S. Rahnamayan, H. Sun, and M. G. H. Omran, “Gaussian bare-bones differential evolution,” IEEE Transactions on Cybernetics, vol. 43, no. 2, pp. 634–647, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. A. Deb, J. S. Roy, and B. Gupta, “Performance comparison of differential evolution, particle swarm optimization and genetic algorithm in the design of circularly polarized microstrip antennas,” IEEE Transactions on Antennas and Propagation, vol. 62, no. 8, pp. 3920–3928, 2014. View at Publisher · View at Google Scholar
  11. J. Brest, S. Greiner, B. Bošković, M. Mernik, and V. Zumer, “Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 6, pp. 646–657, 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Zhang and A. C. Sanderson, “Jade: adaptive differential evolution with optional external archive,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 5, pp. 945–958, 2009. View at Publisher · View at Google Scholar
  13. R. Mallipeddi, P. N. Suganthan, Q. K. Pan, and M. F. Tasgetiren, “Differential evolution algorithm with ensemble of parameters and mutation strategies,” Applied Soft Computing Journal, vol. 11, no. 2, pp. 1679–1696, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. 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
  15. H. Wang, Z. Wu, and S. Rahnamayan, “Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems,” Soft Computing, vol. 15, no. 11, pp. 2127–2140, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. D. Jia, G. Zheng, and M. Khurram Khan, “An effective memetic differential evolution algorithm based on chaotic local search,” Information Sciences, vol. 181, no. 15, pp. 3175–3187, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. Y. Wang, Z. Cai, and Q. Zhang, “Differential evolution with composite trial vector generation strategies and control parameters,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 55–66, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 67–82, 1997. View at Publisher · View at Google Scholar · View at Scopus
  19. N. Noman and H. Iba, “Accelerating differential evolution using an adaptive local search,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 1, pp. 107–125, 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. Q.-K. Pan, P. N. Suganthan, L. Wang, L. Gao, and R. Mallipeddi, “A differential evolution algorithm with self-adapting strategy and control parameters,” Computers and Operations Research, vol. 38, no. 1, pp. 394–408, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  21. D. Kranjcic and G. Stumberger, “Differential evolution-based identification of the nonlinear kaplan turbine model,” IEEE Transactions on Energy Conversion, vol. 29, no. 1, pp. 178–187, 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. Q.-K. Pan, L. Wang, L. Gao, and W. D. Li, “An effective hybrid discrete differential evolution algorithm for the flow shop scheduling with intermediate buffers,” Information Sciences, vol. 181, no. 3, pp. 668–685, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. L. X. Tang, Y. Zhao, and J. Y. Liu, “An improved differential evolution algorithm for practical dynamic scheduling in steelmaking-continuous casting production,” IEEE Transactions on Evolutionary Computation, vol. 18, no. 2, pp. 209–225, 2014. View at Publisher · View at Google Scholar · View at Scopus
  24. S. Das, A. Abraham, U. K. Chakraborty, and A. Konar, “Differential evolution using a neighborhood-based mutation operator,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 3, pp. 526–553, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. M. G. Epitropakis, D. K. Tasoulis, N. G. Pavlidis, V. P. Plagianakos, and M. N. Vrahatis, “Enhancing differential evolution utilizing proximity-based mutation operators,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 99–119, 2011. View at Publisher · View at Google Scholar · View at Scopus
  26. S. Kundu, S. Das, A. V. Vasilakos, and S. Biswas, “A modified differential evolution-based combined routing and sleep scheduling scheme for lifetime maximization of wireless sensor networks,” Soft Computing, vol. 19, no. 3, pp. 637–659, 2014. View at Google Scholar
  27. T. Bhadra and S. Bandyopadhyay, “Unsupervised feature selection using an improved version of differential evolution,” Expert Systems with Applications, vol. 42, no. 8, pp. 4042–4053, 2015. View at Publisher · View at Google Scholar
  28. Y. Wang, Z. Cai, and Q. Zhang, “Enhancing the search ability of differential evolution through orthogonal crossover,” Information Sciences, vol. 185, no. 1, pp. 153–177, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  29. 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
  30. B. Li and W. S. Jiang, “Optimizing complex functions by chaos search,” Cybernetics & Systems, vol. 29, no. 4, pp. 409–419, 1998. View at Publisher · View at Google Scholar · View at Scopus
  31. Y. He, Q. Xu, S. Yang, and L. Liao, “Reservoir flood control operation based on chaotic particle swarm optimization algorithm,” Applied Mathematical Modelling, vol. 38, no. 17, pp. 4480–4492, 2014. View at Publisher · View at Google Scholar · View at Scopus
  32. B. Liu, L. Wang, Y.-H. Jin, F. Tang, and D.-X. Huang, “Improved particle swarm optimization combined with chaos,” Chaos, Solitons & Fractals, vol. 25, no. 5, pp. 1261–1271, 2005. View at Publisher · View at Google Scholar · View at Scopus
  33. T. Xiang, X. Liao, and K.-W. Wong, “An improved particle swarm optimization algorithm combined with piecewise linear chaotic map,” Applied Mathematics and Computation, vol. 190, no. 2, pp. 1637–1645, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  34. X. F. Yan, D. Z. Chen, and S. X. Hu, “Chaos-genetic algorithms for optimizing the operating conditions based on RBF-PLS model,” Computers & Chemical Engineering, vol. 27, no. 10, pp. 1393–1404, 2003. View at Publisher · View at Google Scholar · View at Scopus
  35. X. Yao, Y. Liu, and G. Lin, “Evolutionary programming made faster,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 2, pp. 82–102, 1999. View at Publisher · View at Google Scholar · View at Scopus
  36. Y.-W. Shang and Y.-H. Qiu, “A note on the extended Rosenbrock function,” Evolutionary Computation, vol. 14, no. 1, pp. 119–126, 2006. View at Publisher · View at Google Scholar · View at Scopus
  37. S. Garcia and F. Herrera, “An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons,” Journal of Machine Learning Research, vol. 9, pp. 2677–2694, 2008. View at Google Scholar
  38. S. García, D. Molina, M. Lozano, and F. Herrera, “A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: a case study on the CEC'2005 Special Session on Real Parameter Optimization,” Journal of Heuristics, vol. 15, no. 6, pp. 617–644, 2009. View at Publisher · View at Google Scholar · View at Scopus
  39. H. Wang, H. Sun, C. Li, S. Rahnamayan, and J.-S. Pan, “Diversity enhanced particle swarm optimization with neighborhood search,” Information Sciences, vol. 223, pp. 119–135, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus