Table of Contents
Journal of Artificial Evolution and Applications
Volume 2008, Article ID 289564, 12 pages
http://dx.doi.org/10.1155/2008/289564
Research Article

What Else Is the Evolution of PSO Telling Us?

Department of Computer Science, Faculty of Mathematics and Computer Science, Babeş-Bolyai University, Kogălniceanu 1, Cluj-Napoca 400084, Romania

Received 20 July 2007; Revised 29 November 2007; Accepted 29 November 2007

Academic Editor: Alex Freitas

Copyright © 2008 Laura Dioşan and Mihai Oltean. 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. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Boston, Mass, USA, 1989.
  2. J. H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, Mich, USA, 1975.
  3. R. C. Eberhart and Y. Shi, “Particle swarm optimization: developments, applications, and resources,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '01), vol. 1, pp. 81–86, IEEE Press, Seoul, Korea, May 2001. View at Publisher · View at Google Scholar
  4. M. Dorigo, V. Maniezzo, and A. Colorni, “Ant system: optimization by a colony of cooperating agents,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 26, no. 1, pp. 29–41, 1996. View at Publisher · View at Google Scholar · View at PubMed
  5. J. Kennedy, “The behavior of particles,” in Proceedings of the 7th International Conference on Evolutionary Programming VII (EP '98), @#editos, vol. 1447 of Lecture Notes in Computer Science, pp. 581–589, Springer, San Diego, Calif, USA, 1998.
  6. J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks (ICNN '95), pp. 1942–1948, IEEE Service Center, Perth, Australia, November-December 1995.
  7. J. Kennedy and R. C. Eberhart, “The particle swarm: social adaptation in information-processing systems,” in New Ideas in Optimization, D. Corne, M. Dorigo, and F. Glaover, Eds., pp. 379–387, McGraw-Hill, London, UK, 1999.
  8. X. Hu, Y. Shi, and R. Eberhart, “Recent advances in particle swarm,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '04), vol. 1, pp. 90–97, IEEE Press, Portland, Ore, USA, June 2004.
  9. Y. Shi and R. C. Eberhart, “Empirical study of particle swarm optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '99), P. J. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, and A. Zalzala, Eds., vol. 3, pp. 1945–1950, IEEE Service Center, Washington, DC, USA, July 1999.
  10. L. Dioşan and M. Oltean, “Evolving the structure of the particle swarm optimization algorithms,” in Proceedings of the 6th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP '06), vol. 3906 of Lecture Notes in Computer Science, pp. 25–36, Budapest, Hungary, April 2006.
  11. A. Carlisle and G. Dozier, “An off-the-shelf pso,” in Proceedings of the Particle Swarm Optimization Workshop, pp. 1–6, Indianapolis, Ind, USA, April 2001.
  12. B.-I. Koh, A. D. George, R. T. Haftka, and B. J. Fregly, “Parallel asynchronous particle swarm optimization,” International Journal for Numerical Methods in Engineering, vol. 67, no. 4, pp. 578–595, 2006. View at Publisher · View at Google Scholar · View at PubMed
  13. R. Poli, W. B. Langdon, and O. Holland, “Extending particle swarm optimisation via genetic programming,” in Proceedings of the 8th European Conference on Genetic Programming (EuroGP '05), vol. 3447 of Lecture Notes in Computer Science, pp. 291–300, Lausanne, Switzerland, March-April 2005.
  14. C. A. Coello Coello and M. Salazar Lechuga, “MOPSO: a proposal for multiple objective particle swarm optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '02), vol. 2, pp. 1051–1056, IEEE Service Center, Honolulu, Hawaii, USA, May 2002.
  15. X. Hu and R. Eberhart, “Multi-objective optimization using dynamic neighborhood particle swarm optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '02), vol. 2, pp. 1677–1681, IEEE Service Center, Honolulu, Hawaii, USA, May 2002.
  16. X. Hu, R. C. Eberhart, and Y. Shi, “Swarm intelligence for permutation optimization: case study of n-queens problem,” in Proceedings of the IEEE Swarm Intelligence Symposium (SIS '03), Y. Com, Ed., pp. 243–246, Indianapolis, Ind, USA, April 2003. View at Publisher · View at Google Scholar
  17. J. Kennedy and R. Eberhart, “A discrete binary version of the particle swarm algorithm,” in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, vol. 5, pp. 4104–4108, IEEE Service Center, Orlando, Fla, USA, October 1997.
  18. C. K. Mohan and B. Al-kazemi, “Discrete particle swarm optimization,” in Proceedings of the Particle Swarm Optimization Workshop, Indianapolis, Ind, USA, April 2001.
  19. D. Srinivasan and T. H. Seow, “Particle swarm inspired evolutionary algorithm (PS-EA) for multi-objective optimization problem,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '03), R. Sarker, R. Reynolds, and H. Abbass, Eds., pp. 2292–2297, IEEE Press, Canbella, Australia, December 2003.
  20. 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
  21. W. B. Langdon, R. Poli, and C. R. Stephens, “Kernel methods for PSOs,” Tech. Rep., Computer Science, University of Essex, UK, 2005.
  22. E. Ozcan and C. Mohan, “Particle swarm optimization: surfing the waves,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '99), pp. 1939–1944, IEEE Service Center, Washington, DC, USA, July 1999.
  23. F. van den Bergh, An Analysis of Particle Swarm Optimizers, Ph.D. thesis, Department of Computer Science, University of Pretoria, Pretoria, South Africa, 2002.
  24. T. Hendtlass, “A combined swarm differential evolution algorithm for optimization problems,” in Proceedings of the 14th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE '01), L. Monostori, J. Váncza, and M. Ali, Eds., vol. 2070 of Lecture Notes in Computer Science, pp. 11–18, Budapest, Hungary, June 2001.
  25. H. Kwong and C. Jacob, “Evolutionary exploration of dynamic swarm behaviour,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '03), R. Sarker, R. Reynolds, and H. Abbass, Eds., pp. 367–374, IEEE Press, Canbella, Australia, December 2003.
  26. 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
  27. A. E. M. Zavala, A. H. Aguirre, and E. R. V. Diharce, “Particle evolutionary swarm optimization algorithm (PESO),” in Proceedings of the 6th Mexican International Conference on Computer Science (ENC '05), vol. 2005, pp. 282–289, IEEE Computer Society, Guanajuato, Mexico, September 2005. View at Publisher · View at Google Scholar
  28. W.-J. Zhang and X.-F. Xie, “DEPSO: hybrid particle swarm with differential evolution operator,” in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3816–3821, IEEE Press, Washington, DC, USA, October 2003.
  29. R. Storn and K. Price, “Differential evolution—a simple and effcient adaptive scheme for global optimization over continuous spaces,” Tech. Rep., International Computer Science Institute, Berkeley, Calif, USA, 1995.
  30. M. Oltean, “Solving even-parity problems using multi expression programming,” in Proceedings of the 7th Joint Conference on Information Sciences, K. Chen, Ed., vol. 1, pp. 315–318, Association for Intelligent Machinery, Cary, NC, USA, September 2003.
  31. M. Oltean and C. Grosan, “Evolving evolutionary algorithms using multi expression programming,” in Proceedings of the 7th European Conference on Artificial Life (ECAL '03), W. Banzhaf, T. Christaller, P. Dittrich, J. T. Kim, and J. Ziegler, Eds., vol. 2801 of Lecture Notes in Computer Science, pp. 651–658, Springer, Dortmund, Germany, September 2003.
  32. M. Oltean, “Evolving evolutionary algorithms using linear genetic programming,” Evolutionary Computation, vol. 13, no. 3, pp. 387–410, 2005. View at Publisher · View at Google Scholar · View at PubMed
  33. M. Brameier and W. Banzhaf, “A comparison of linear genetic programming and neural networks in medical data mining,” IEEE Transactions on Evolutionary Computation, vol. 5, no. 1, pp. 17–26, 2001. View at Publisher · View at Google Scholar
  34. M. Brameier and W. Banzhaf et al., “Evolving teams of predictors with linear genetic programming,” Genetic Programming and Evolvable Machines, vol. 2, no. 4, pp. 381–407, 2001. View at Publisher · View at Google Scholar
  35. M. Brameier and W. Banzhaf, “Explicit control of diversity and effective variation distance in linear genetic programming,” in Proceedings of the 5th European Conference on Genetic Programming (EuroGP '02), vol. 2278 of Lecture Notes in Computer Science, pp. 37–49, Kinsale, Ireland, April 2002.
  36. G. Syswerda, “A study of reproduction in generational and steady state genetic algorithms,” in Foundations of Genetic Algorithms, G. J. E. Rawlins, Ed., pp. 94–101, Morgan Kaufmann, San Francisco, Calif, USA, 1991.
  37. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolutionprograms, Springer, NewYork, NY, USA, 1996.
  38. B. L. Miller and D. E. Goldberg, “Genetic algorithms, tournament selection, and the effects of noise,” Complex Systems, vol. 9, pp. 193–212, 1995.
  39. L. J. Eshelman, “The CHC adaptive search algorithm: how to have safe search when engaging in non-traditional genetic recombination,” in Foundations of Genetic Algorithms, G. J. E. Rawlins, Ed., pp. 265–283, Morgan Kaufmann, San Francisco, Calif, USA, 1991.
  40. W. E. Howden, “Weak mutation testing and completeness of test sets,” IEEE Transactions on Software Engineering, vol. 8, no. 4, pp. 371–379, 1982. View at Publisher · View at Google Scholar
  41. M. Clerc, Particle Swarm Optimization, ISTE, London, UK, 2006.
  42. 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
  43. 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