Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 761403, 14 pages
http://dx.doi.org/10.1155/2014/761403
Research Article

A Novel Adaptive Elite-Based Particle Swarm Optimization Applied to VAR Optimization in Electric Power Systems

1Department of Electrical Engineering, Chung Yuan Christian University, Chung Li City 320, Taiwan
2Department of Electrical Engineering, National Central University, Chung Li City 320, Taiwan
3Department of Applied Electronics Technology, National Taiwan Normal University, Taipei 320, Taiwan

Received 5 February 2014; Accepted 30 March 2014; Published 22 May 2014

Academic Editor: Ker-Wei Yu

Copyright © 2014 Ying-Yi Hong 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. 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 (MHS '95), pp. 39–43, Nagoya, Japan, October 1995. View at Scopus
  2. C. Wang, Y. C. Liu, and Y. T. Zhao, “Application of dynamic neighborhood small population particles warm optimization for reconfiguration of shipboard power system,” Engineering Applications of Artificial Intelligence, vol. 26, no. 4, pp. 1255–1262, 2013. View at Publisher · View at Google Scholar
  3. M. Neyestani, M. M. Farsangi, and H. Nezamabadi-Pour, “A modified particle swarm optimization for economic dispatch with non-smooth cost functions,” Engineering Applications of Artificial Intelligence, vol. 23, no. 7, pp. 1121–1126, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. B. Vasumathi and S. Moorthi, “Implementation of hybrid ANNPSO algorithm on FPGA for harmonic estimation,” Engineering Applications of Artificial Intelligence, vol. 25, no. 3, pp. 476–483, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. W. X. Liu, I.-Y. Chung, L. Liu, S. Y. Leng, and D. A. Cartes, “Real-time particle swarm optimization based current harmonic cancellation,” Engineering Applications of Artificial Intelligence, vol. 24, no. 1, pp. 132–141, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. R. J. Wai, Y. C. Huang, Y. C. Chen, and Y. W. Lin, “Performance comparisons of intelligent load forecasting structures and its application to energy-saving load regulation,” Soft Computing, vol. 17, no. 10, pp. 1797–1815, 2013. View at Publisher · View at Google Scholar
  7. F. van den Bergh and A. P. Engelbrecht, “A cooperative approach to particle swarm optimization,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 225–239, 2004. View at Publisher · View at Google Scholar
  8. A. Nickabadi, M. M. Ebadzadeh, and R. Safabakhsh, “A novel particle swarm optimization algorithm with adaptive inertia weight,” Applied Soft Computing, vol. 11, no. 4, pp. 3658–3670, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. Z.-H. Zhan, J. Zhang, Y. Li, and H. S.-H. Chung, “Adaptive particle swarm optimization,” IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics, vol. 39, no. 6, pp. 1362–1381, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. R. Caponetto, L. Fortuna, S. Fazzino, and M. G. Xibilia, “Chaotic sequences to improve the performance of evolutionary algorithms,” IEEE Transactions on Evolutionary Computation, vol. 7, no. 3, pp. 289–304, 2003. View at Publisher · View at Google Scholar · View at Scopus
  11. G. N. José and A. Enrique, “Restart particle swarm optimization with velocity modulation: a scalability test,” Soft Computing, vol. 15, no. 11, pp. 2221–2232, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. W. Deng, R. Chen, B. He, Y. Liu, L. Yin, and J. Guo, “A novel two-stage hybrid swarm intelligence optimization algorithm and application,” Soft Computing, vol. 16, no. 10, pp. 1707–1722, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. Y. Y. Li, R. R. Xiang, L. C. Jiao, and R. C. Liu, “An improved cooperative quantum-behaved particle swarm optimization,” Soft Computing, vol. 16, no. 6, pp. 1061–1069, 2012. View at Publisher · View at Google Scholar
  14. A. M. Arasomwan and A. O. Adewumi, “An investigation into the performance of particle swarm optimization with various chaotic maps,” Mathematical Problems in Engineering, vol. 2014, Article ID 178959, 17 pages, 2014. View at Publisher · View at Google Scholar · View at MathSciNet
  15. H. Yin, C. Zhang, B. Zhang, Y. Guo, and T. Liu, “A hybrid multiobjective discrete particle swarm optimization algorithm for a SLA-aware service composition problem,” Mathematical Problems in Engineering, vol. 2014, Article ID 252934, 14 pages, 2014. View at Publisher · View at Google Scholar · View at MathSciNet
  16. 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
  17. 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 Zentralblatt MATH · View at MathSciNet · View at Scopus
  18. J. Kennedy and R. Eberhart, Swarm Intelligence, Morgan Kaufmann, San Francisco, Calif, USA, 2001.
  19. S. Bouallègue, J. Haggège, M. Ayadi, and M. Benrejeb, “PID-type fuzzy logic controller tuning based on particle swarm optimization,” Engineering Applications of Artificial Intelligence, vol. 25, no. 3, pp. 484–493, 2012. View at Publisher · View at Google Scholar
  20. N. J. Li, W. J. Wang, C. C. J. Hsu, W. Chang, H. G. Chou, and J. W. Changa, “Enhanced particle swarm optimizer incorporating a weighted particle,” Neurocomputing, vol. 124, pp. 218–227, 2014. View at Publisher · View at Google Scholar
  21. G. A. Ortiz, “Evolution Strategies (ES),” Mathwork, 2012.
  22. A. J. Wood and B. F. Wollenberg, Power Generation, Operation and Control, John Wiley & Son, New York, NY, USA, 2nd edition, 1996.