Table of Contents
Journal of Artificial Evolution and Applications
Volume 2008 (2008), Article ID 827401, 9 pages
http://dx.doi.org/10.1155/2008/827401
Research Article

Novel Orthogonal Momentum-Type Particle Swarm Optimization Applied to Solve Large Parameter Optimization Problems

Department of Information Management, College of Electrical Engineering and Information Science, I-Shou University, No. 1, Section 1, Syuecheng Road, Kaohsiung County 840, Taiwan

Received 13 July 2007; Accepted 10 January 2008

Academic Editor: Jim Kennedy

Copyright © 2008 Jenn-Long Liu and Chao-Chun Chang. 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. J. H. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbor, Mich, USA, 1975.
  2. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, Mass, USA, 1989.
  3. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Program, Springer, Berlin, Germany, 1999.
  4. J. R. Koza, Genetic Programming: on the Programming of Computers by Means of Natural Selection, The MIT Press, Cambridge, Mass, USA, 1992.
  5. L. J. Fogel, “Evolutionary programming in perspective: the top-down view,” in Computational Intelligence: Imitating Life, J. M. Zurada, R. J. Marks II, and C. J. Robinson, Eds., pp. 135–146, IEEE Press, Piscataway, NJ, USA, 1994. View at Google Scholar
  6. D. B. Fogel, “An overview of evolutionary programming,” in Evolutionary Algorithms, L. Davis, K. De Jong, M. Vose, and L. D. Whitley, Eds., IMA Volume in Mathematics and Its Applications, pp. 89–109, Springer, Berlin, Germany, 1999. View at Google Scholar
  7. T. Bäck, F. Hoffmeister, and H.-P. Schwefel, “A survey of evolution strategies,” in Proceedings of the 4th International Conference on Genetic Algorithms (ICGA '91), R. K. Below and L. B. Booker, Eds., pp. 2–9, Morgan Kaufmann, San Diego, Calif, USA, July 1991.
  8. I. Rechenberg, “Evolution strategy,” in Computational Intelligence: Imitating Life, J. M. Zurada, R. J. Marks II, and C. J. Robinson, Eds., IEEE Press, Piscataway, NJ, USA, 1994. View at Google Scholar
  9. J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, Perth, WA, Australia, November-December 1995. View at Publisher · View at Google Scholar
  10. J. Kennedy, R. C. Eberhart, and Y. Shi, Swarm Intelligence, Morgan Kaufmann, San Francisco, Calif, USA, 2001.
  11. R. C. Eberhart and Y. Shi, “Comparison between genetic algorithms and particle swarm optimization,” in Proceedings of the 7th International Conference on Evolutionary Programming (EP '98), vol. 1447 of Lecture Notes in Computer Science, pp. 611–616, San Diego, Calif, USA, March 1998. View at Publisher · View at Google Scholar
  12. Y. Shi and R. C. Eberhart, “Empirical study of particle swarm optimization,” in Proceedings of the Congress on Evolutionary Computation (CEC '99), vol. 3, pp. 1945–1950, Washington, DC, USA, July 1999. View at Publisher · View at Google Scholar
  13. Y. Shi and R. C. Eberhart, “A modified particle swarm optimizer,” in Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC '98), pp. 69–73, Anchorage, Alaska, USA, May 1998. View at Publisher · View at Google Scholar
  14. M. Clerc, “The swarm and the queen: towards a deterministic and adaptive particle swarm optimization,” in In Proceedings of the Congress of Evolutionary Computation (CEC '99), vol. 3, pp. 1951–-1957, Washington, DC, USA, July 1999. View at Publisher · View at Google Scholar
  15. R. C. Eberhart and Y. Shi, “Comparing inertia weights and constriction factors in particle swarm optimization,” in Proceedings of the IEEE Conference on Evolutionary Computation (ICEC '00), vol. 1, pp. 84–88, La Jolla, Calif, USA, July 2000. View at Publisher · View at Google Scholar
  16. K. E. Parsopoulos and M. N. Vrahatis, “Recent approaches to global optimization problems through particle swarm optimization,” Natural Computing, vol. 1, no. 2-3, pp. 235–306, 2002. View at Publisher · View at Google Scholar · View at MathSciNet
  17. J.-L. Liu and J.-H. Lin, “Evolutionary computation of unconstrained and constrained problems using a novel momentum-type particle swarm optimization,” Engineering Optimization, vol. 39, no. 3, pp. 287–305, 2007. View at Publisher · View at Google Scholar · View at MathSciNet
  18. S.-Y. Ho, L.-S. Shu, and J.-H. Chen, “Intelligent evolutionary algorithms for large parameter optimization problems,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 6, pp. 522–541, 2004. View at Publisher · View at Google Scholar
  19. H. S. Lin, Design of a novel particle swarm optimization, M.S. thesis, Department of Information Engineering and Computer Science, Feng Chia University, Taichung, Taiwan, June 2004.
  20. K. R. Bhote, World Class Quality: Using Design of Experiments to Make it Happen, American Management Association Press, New York, NY, USA, 1991.
  21. S. Naka, T. Genji, T. Yura, and Y. Fukuyama, “Practical distribution state estimation using hybrid particle swarm optimization,” in Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference, vol. 2, pp. 815–820, Columbus, Ohio, USA, January-February 2001. View at Publisher · View at Google Scholar
  22. 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
  23. I. C. Trelea, “The particle swarm optimization algorithm: convergence analysis and parameter selection,” Information Processing Letters, vol. 85, no. 6, pp. 317–325, 2003. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  24. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986. View at Google Scholar
  25. G. J. Borse, Numerical Methods with MATLAB, PWS Publishing, Boston, Mass, USA, 1997.