Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 215472, 16 pages
http://dx.doi.org/10.1155/2014/215472
Research Article

An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization

1China Institute of Manufacturing Development, Nanjing University of Information Science & Technology, Nanjing 210044, China
2Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China
3School of Mechanic and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China

Received 30 September 2013; Accepted 17 November 2013; Published 9 February 2014

Academic Editors: T. Chen, Q. Cheng, and J. Yang

Copyright © 2014 Xiaobing Yu 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. 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
  2. M. G. H. Omran, A. P. Engelbrecht, and A. Salman, “Bare bones differential evolution,” European Journal of Operational Research, vol. 196, no. 1, pp. 128–139, 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the 1995 IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, December 1995. 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, pp. 39–43, Nagoya, Japan, 1995.
  5. F. van den Bergh and A. P. Engelbrecht, “A cooperative approach to participle swam optimization,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 225–239, 2004. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Tang, Z. Wang, H. Gao, S. Swift, and J. Kurths, “A constrained evolutionary computation method for detecting controlling regions of cortical networks,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 6, pp. 1569–1581, 2012. View at Google Scholar
  7. Y. Tang, Z. Wang, and J. Fang, “Controller design for synchronization of an array of delayed neural networks using a controllable probabilistic PSO,” Information Sciences, vol. 181, no. 20, pp. 4715–4732, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. Y. Tang, Z. Wang, and J. Fang, “Parameters identification of unknown delayed genetic regulatory networks by a switching particle swarm optimization algorithm,” Expert Systems with Applications, vol. 38, no. 3, pp. 2523–2535, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. S. Y. S. Leung, Y. Tang, and W. K. Wong, “A hybrid particle swarm optimization and its application in neural networks,” Expert Systems with Applications, vol. 39, no. 1, pp. 395–405, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. Y. P. Chen, W. C. Peng, and M. C. Jian, “Particle swarm optimization with recombination and dynamic linkage discovery,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 37, no. 6, pp. 1460–1470, 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Clerc and J. Kennedy, “The particle swarm-explosion, stability, and convergence in a multidimensional complex space,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 58–73, 2002. View at Publisher · View at Google Scholar · View at Scopus
  12. R. C. Eberhart and Y. Shi, “Particle swarm optimization: developments, applications and resources,” in Proceedings of the IEEE Congress on Evolutionary Computation, pp. 81–86, Seoul, Republic of Korea, May 2001. View at Scopus
  13. R. Mendes, J. Kennedy, and J. Neves, “The fully informed particle swarm: simpler, maybe better,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 204–210, 2004. View at Publisher · View at Google Scholar · View at Scopus
  14. J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 3, pp. 281–295, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. R. A. Krohling and L. dos Santos Coelho, “Coevolutionary particle swarm optimization using gaussian distribution for solving constrained optimization problems,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 36, no. 6, pp. 1407–1416, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Ratnaweera, S. K. Halgamuge, and H. C. Watson, “Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 240–255, 2004. View at Publisher · View at Google Scholar · View at Scopus
  17. Y. Shi and R. C. Eberhart, “Empirical study of particle swarm optimization,” in Proceedings of the 1999 IEEE Congress on Evolutionary Computation, pp. 1945–1950, IEEE Press, Piscataway, NJ, USA, 1999.
  18. Y. Shi and R. C. Eberhart, “Parameter selection in particle swarm optimization,” in Proceedings of the 7th International Conference on Evolutionary Programming VII (LNCS '98), pp. 591–600, Springer, New York, NY, USA, 1998.
  19. Z. Zhan, J. Zhang, Y. Li, and H. S. H. Chung, “Adaptive particle swarm optimization,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 39, no. 6, pp. 1362–1381, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. 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
  21. J. Liu and J. Lampinen, “On setting the control parameter of the differential evolution method,” in Proceedings of the 8th International Conference on Soft Computing (MENDEL '02), pp. 11–18, 2002.
  22. Y. Tang, H. Gao, W. Zou, and J. Kurths, “Identifying controlling nodes in neuronal networks in different scales,” PLoS ONE, vol. 7, no. 7, Article ID e41375, 2012. View at Google Scholar
  23. 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
  24. S. Das and A. Konar, “Automatic image pixel clustering with an improved differential evolution,” Applied Soft Computing Journal, vol. 9, no. 1, pp. 226–236, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. R. Storn, “Differential evolution design of an IIR-filter,” in Proceedings of the 1996 IEEE International Conference on Evolutionary Computation (ICEC '96), pp. 268–273, May 1996. View at Scopus
  26. M. Varadarajan and K. S. Swarup, “Differential evolution approach for optimal reactive power dispatch,” Applied Soft Computing Journal, vol. 8, no. 4, pp. 1549–1561, 2008. View at Publisher · View at Google Scholar · View at Scopus
  27. K. Price, R. Storn, and J. Lampinen, Differential Evolution—A Practical Approach to Global Optimization, Springer, Berlin, Germany, 2005.
  28. R. Mallipeddi and P. N. Suganthan, “Differential evolution algorithm with ensemble ofparameters and mutation and crossover strategies,” in Proceedings of the Swarm Evolutionary and Memetic Computing Conference, vol. 6466, pp. 71–78, Chennai, India, 2010.
  29. 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
  30. Z. Yang, K. Tang, and X. Yao, “Self-adaptive differential evolution with neighborhood search,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '08), pp. 1110–1116, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  31. 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 · View at Scopus
  32. M. Pant and R. Thangaraj, “DE-PSO: a new hybrid meta-heuristic for solving global optimization problems,” New Mathematics and Natural Computation, vol. 7, no. 3, pp. 363–381, 2011. View at Google Scholar
  33. S. Kannan, S. M. R. Slochanal, P. Subbaraj, and N. P. Padhy, “Application of particle swarm optimization technique and its variants to generation expansion planning problem,” Electric Power Systems Research, vol. 70, no. 3, pp. 203–210, 2004. View at Publisher · View at Google Scholar · View at Scopus
  34. H. Talbi and M. Batouche, “Hybrid particle swarm with differential evolution for multimodal image registration,” in Proceedings of the IEEE International Conference on Industrial Technology (ICIT '04), vol. 3, pp. 1567–1572, December 2004. View at Scopus
  35. M. G. H. Omran, A. P. Engelbrecht, and A. Salman, “Differential evolution based particle swarm optimization,” in Proceedings of the IEEE Swarm Intelligence Symposium (SIS '07), pp. 112–119, April 2007. View at Publisher · View at Google Scholar · View at Scopus
  36. Z. F. Hao, G. H. Guo, and H. Huang, “A particle swarm optimization algorithm with differential evolution,” in Proceedings of the 6th International Conference on Machine Learning and Cybernetics (ICMLC '07), pp. 1031–1035, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  37. Y. Tang, Z. D. Wang, and J. A. Fang, “Feedback learning particle swarm optimization,” Applied Soft Computing Journal, vol. 11, no. 8, pp. 4713–4725, 2011. View at Publisher · View at Google Scholar · View at Scopus
  38. P. K. Tripathi, S. Bandyopadhyay, and S. K. Pal, “Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients,” Information Sciences, vol. 177, no. 22, pp. 5033–5049, 2007. View at Publisher · View at Google Scholar · View at Scopus
  39. 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
  40. H. Sharma, J. C. Bansal, and K. V. Arya, “Fitness based differential evolution,” Memetic Computing, vol. 4, pp. 303–316, 2012. View at Google Scholar
  41. D. Bratton and J. Kennedy, “Defining a standard for particle swarm optimization,” in Proceedings of the IEEE Swarm Intelligence Symposium (SIS '07), pp. 120–127, April 2007. View at Publisher · View at Google Scholar · View at Scopus
  42. T. Peram, K. Veeramachaneni, and C. K. Mohan, “Fitness-distance-ratio based particle swarm optiniization,” in Proceedings of the 2003 IEEE Swarm Intelligence Symposium (SIS '03), pp. 174–181, 2003.
  43. M. M. Ali and A. Törn, “Population set-based global optimization algorithms: some modifications and numerical studies,” Computers and Operations Research, vol. 31, no. 10, pp. 1703–1725, 2004. View at Publisher · View at Google Scholar · View at Scopus
  44. M. Ali and M. Pant, “Improving the performance of differential evolution algorithm using Cauchy mutation,” Soft Computing, vol. 15, no. 5, pp. 991–1007, 2011. View at Publisher · View at Google Scholar · View at Scopus
  45. A. K. Qin and P. N. Suganthan, “Self-adaptive differential evolution algorithm for numerical optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '05), vol. 2, pp. 1785–1791, September 2005. View at Scopus