Advertisement

Evolutionary Particle Swarm Optimization: A Metaoptimization Method with GA for Estimating Optimal PSO Models

  • Hong Zhang
  • Masumi Ishikawa
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 6)

Particle swarm optimization (PSO) is an algorithm for swarm intelligence based on stochastic and population-based adaptive optimization inspired by social behavior of bird flocks and fish swarms [5, 10].

To demonstrate the effectiveness of the proposed EPSO method, computer experiments on a two-dimensional optimization problem are carried out. We show experimental results, confirm the characteristics of dependency on initial conditions, and analyze the resulting PSO models.

The rest of the chapter is organized as follows. Section 5.2 briefly describes the original PSO and RGA/E. Section 5.3 presents the proposed EPSO method and a key idea about the temporally cumulative fitness that we used in the method. Section 5.4 discusses the results of computer experiments applied to a two-dimensional optimization problem and Sect. 5.5 gives conclusions.

Keywords

Particle Swarm Optimization Particle Swarm Differential Evolution Global Optimal Solution Success Ratio 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bergh F and Engelbrecht AP (2001) Effects of swarm size on cooperative partical swarm optimisers. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO2001), Morgan Kaufmann, San Francisco, CA, 892–899Google Scholar
  2. 2.
    Beielstein T, Parsopoulos KE, and Vrahatis MN (2002) Tuning PSO parameters through sensitivity analysis, Technical report of the Collaborative Research Center 531 Computational Intelligence CI-124/02, University of DortmundGoogle Scholar
  3. 3.
    Carlisle A and Dozier G (2001) An off-the-shelf PSO. Proceedings of the Workshop on Particle Swarm Optimization Indianapolis, 1–6Google Scholar
  4. 4.
    Dorigo M, Maniezzo V, and Colorni A (1996) The ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics-Part B, 1:1–13Google Scholar
  5. 5.
    Eberhart RC and Kennedy J (1995) A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science Nagoya, Japan, 39–43Google Scholar
  6. 6.
    Eberhart RC and Shi Y (2000) Comparing inertia weights and constriction factors in particleswarm optimization. Proceedings of the 2000 IEEE Congress on Evolutionary Computation La Jolla, CA, 1:84–88Google Scholar
  7. 7.
    Eshelman LJ and Schaffer JD (1993) Real-coded genetic algorithms and interval-schemata. Foundations of Genentic Algorithms Morgan Kaufman, San Mateo, CA, 2:187–202Google Scholar
  8. 8.
    Goldberg DE (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, BostonMATHGoogle Scholar
  9. 9.
    Gudise VG and Venayagamoorthy GK (2003) Evolving digital circuits using particle swarm. Neural Networks. Proceedings of the International Joint Conference on Special Issue 1:468–472Google Scholar
  10. 10.
    Kennedy J and Eberhart RC (1995) Particle swarm optimization. Proceedings of the 1995 IEEE International Conference on Neural Networks Piscataway, NJ, 1942–1948Google Scholar
  11. 11.
    Kennedy J (2006) In search of the essential particle swarm. Proceedings of 2006 IEEE Congress on Evolutionary Computations, Vancouver, BC, 6158–6165Google Scholar
  12. 12.
    Meissner M, Schmuker M, and Schneider G (2006) Optimized particle swarm optimization (OPSO) and its application to artificial neural network training. BMC Bioinformatics 7:125–135CrossRefGoogle Scholar
  13. 13.
    Parsopoulos KE and Vrahatis MN (2002) Recent approaches to global optimization problems through particle swarm optimization. Natural Computing 1:235–306MATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Reyes-Sierra M and Coello Coello CA (2006) Multi-objective particle swarm optimizers: A Survey of the state-of-the-art. International Journal of Computational Intelligence Research 2(3):287–308MathSciNetGoogle Scholar
  15. 15.
    Spina R (2006) Optimisation of injection moulded parts by using ANN-PSO approach. Journal of Achievements in Materials and Manufacturing Engineering 15(1–2):146–152Google Scholar
  16. 16.
    Storn R and Price K (1997) Differential evolution—A simple and efficient heuristic for global optimization over continuous space. Journal of Global Optimization 11:341–359MATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Zhang H and Ishikawa M (2005) A hybrid real–coded genetic algorithm with local search. Proceedings of the 12th International Conference on Neural Information Processing (ICONIP2005) Taipei, Taiwan R.O.C, 732–737Google Scholar
  18. 18.
    Xie XF, Zhang WJ, and Yang ZL (2002) A disspative particle swarm optimization. Proceedings of the IEEE Congress on Evolutionary Computation (CEC20020) Honolulu, 1456–1461Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Hong Zhang
    • 1
  • Masumi Ishikawa
    • 1
  1. 1.Kyushu Institute of TechnologyWakamatsuJapan

Personalised recommendations