Skip to main content
Book cover

Hybrid Self-Organizing Modeling Systems pp 193–231Cite as

Hybrid Particle Swarm Optimization and GMDH System

  • Chapter

Part of the Studies in Computational Intelligence book series (SCI,volume 211)

Abstract

This chapter describes a new design methodology which is based on hybrid of particle swarm optimization (PSO) and group method of data handling (GMDH). The PSO and GMDH are two well-known nonlinear methods of mathematical modeling. This novel method constructs a GMDH network model of a population of promising PSO solutions. The new PSO-GMDH hybrid implementation is then applied to modeling and prediction of practical datasets and its results are compared with the results obtained by GMDH-related algorithms. Results presented show that the proposed algorithm appears to perform reasonably well and hence can be applied to real-life prediction and modeling problems.

Keywords

  • Particle Swarm Optimization
  • Particle Swarm
  • Tool Wear
  • Particle Swarm Optimization Algorithm
  • Current Layer

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.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-01530-4_5
  • Chapter length: 39 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   149.00
Price excludes VAT (USA)
  • ISBN: 978-3-642-01530-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   199.99
Price excludes VAT (USA)
Hardcover Book
USD   219.99
Price excludes VAT (USA)
Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Farlow, S.J.: The GMDH Algorithm of Ivakhnenko. The American Statistician 35(4) (1981)

    Google Scholar 

  2. Hamming, R.W., Feigenbaum, E.A.: Interpolation and Roundoff Estimation. In: Introduction to Applied Numerical Analysis, pp. 143–148. McGraw-Hill, New York (1971)

    Google Scholar 

  3. Zaychenko, Y.P., Kebkal, A.G., Krachkovckii, V.F.: The Fuzzy Group Method of Data Handling and its Application to the Problems of the Macroeconomic Indexes Forecasting (2007), http://www.gmdh.net/

  4. Neapolitan, R.E., Naimipour, K.: The greedy approach, Fondation of Algorithms using C++ Pseudocode. Jones and Bartlet publishers Inc. (2003)

    Google Scholar 

  5. Onwubolu, G.C.: Design of Hybrid Differential Evolution and Group Method of Data Handling for Inductive Modeling. In: International Workshop on Inductive Modeling, IWIM Prague, Czech, pp. 23–26 (2007)

    Google Scholar 

  6. Kreyszig, E.: Unconstrained optimization, linear programming. In: Advance Engineering Mathematics, 2nd edn. John Wiley, Inc., Chichester (1993)

    Google Scholar 

  7. Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proc. Sixth International Symposium on Micro Machine and human science, Nagoya, Japan. IEEE Service Center, Piscataway (1995)

    Google Scholar 

  8. Clerc, M.: Discrete particle swarm optimization illustrated by the traveling salesman problem. In: New Optimization techniques in Engineering. Springer, Berlin (2004)

    Google Scholar 

  9. Carlistle, A., Dozier, G.: Adapting Particle Swarm Optimization to Dynamic Environments (1998), http://www.CartistleA.edu

  10. Kennedy, J., Eberhart, R.C.: The particle swarm: social adaptation in information processing systems. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 379–387. McGraw-Hill, London (1999)

    Google Scholar 

  11. Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: International Conference on Systems, Man, and Cybernetics (1997)

    Google Scholar 

  12. Kennedy, J.: The particle swarm: social adaptation of knowledge. In: IEEE international conference on Evolutionary computation, indianpolis, Indiana. IEEE Service Center, Piscataway (1997)

    Google Scholar 

  13. Clerc, M., Kennedy, J.: The particle swarm: explosion, stability, and convergence in a multidimensional complex space. IEEE transactions on Evolutionary Computation 6, 58–73 (2002)

    CrossRef  Google Scholar 

  14. Kennedy, J.: Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Congress on Evolutionary computation, Washington D.C. IEEE, Los Alamitos (1999)

    Google Scholar 

  15. Clerc, M.: The Swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In: Congress on Evolutionary Computation, Washington D.C, pp. 1951–1957. IEEE Service Center, Piscataway (1999)

    Google Scholar 

  16. Kennedy, J.: Stereotyping: Improving Particle Swarm Performance with Cluster Analysis. Presented at Congress on Evolutionary Computation (2000)

    Google Scholar 

  17. Kennedy, J., Spears, W.: Matching algorithms to problems: An experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 1998), Anchorage, Alaska (1998)

    Google Scholar 

  18. Onwubolu, G.C., Sharma, A.: Particle Swarm Optimization for the assignment of facilities to locations. In: New Optimization Techniques in Engineering. Springer, Heidelberg (2004)

    Google Scholar 

  19. He, Z., Wei, C.: A new population-based incremental learning method for the traveling salesman problem. In: Congress on Evolutionary Computation, Washington D.C. IEEE, Los Alamitos (1999)

    Google Scholar 

  20. Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical recipes in C: The art of scientific computing. Cambridge University Press, Cambridge (1992)

    Google Scholar 

  21. Nariman-Zadeh, N., Darvizeh, A., Ahmad-Zadeh, G.R.: Hybrid genetic design of GMDH-type neural networks using singular value decomposition for modeling and predicting of the explosive cutting process, Nariman-Zadeh. In: Proc. Instn Mech. Engrs Vol 217 Part B: Nariman-Zadeh, 779–790 (2003)

    Google Scholar 

  22. Ivakhnenko, A.G.: The Group Method of Data Handling-A rival of the Method of Stochastic Approximation. Soviet Automatic Control, vol 13 c/c of avtomatika 1(3), 43–55 (1968)

    Google Scholar 

  23. Larson, R., Edward, B.H., Falvo, D.C.: Application of Matrix Operations, 5th edn. Elementary Linear Algebra, pp. 107–110. Houghton Mifflin, New York (2004)

    Google Scholar 

  24. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  25. Glover, F.: Heuristics for interger programming using surrogate constraints. Decision Sciences 8, 156–166 (1977)

    CrossRef  Google Scholar 

  26. Kirkpatrick, S., Gelatt, C.D., Vecci, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)

    CrossRef  MathSciNet  Google Scholar 

  27. Dorigo, M.: Optimization, Learning and Natural Algorithm, PhD thesis, Politecnico di Milano, Italy (1992)

    Google Scholar 

  28. Box, G.E.P., Jenkins, G.M.: Time Series Analysis, Forecasting and Control, pp. 532–533. Holden Day, San Francisco (1970)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of School of Computing, Information System, Mathematical Sciences and Statistics, Faculty of Science & Technology, The University of the South Pacific, Private Bag, Suva, Fiji

    Anurag Sharma

  2. Knowledge Management & Mining, Richmond Hill, Canada

    Godfrey Onwubolu

Authors
  1. Anurag Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Godfrey Onwubolu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Knowledge Management & Mining Inc., Richmond Hill, Ontario, Canada

    Godfrey C. Onwubolu

Rights and permissions

Reprints and Permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Sharma, A., Onwubolu, G. (2009). Hybrid Particle Swarm Optimization and GMDH System. In: Onwubolu, G.C. (eds) Hybrid Self-Organizing Modeling Systems. Studies in Computational Intelligence, vol 211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01530-4_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-01530-4_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01529-8

  • Online ISBN: 978-3-642-01530-4

  • eBook Packages: EngineeringEngineering (R0)