Skip to main content
SpringerLink
Log in

Gravitational Search Algorithm with Parameter Adaptation Through a Fuzzy Logic System

  • Chapter
  • First Online:
Nature-Inspired Design of Hybrid Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 667))

Abstract

The contribution of this paper is to provide an analysis of the parameters of Gravitational Search Algorithm (GSA), to include a fuzzy logic system for dynamic parameter adaptation through the execution of the algorithm, in order to control the behavior of GSA based on some metrics like the iterations and the diversity of the agents in an specific moment of its execution.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bahrololoum, A.; Nezamabadi-pour, Bahrololoum, H.; Saeed, M. “A prototype classifier based on gravitational search algorithm”, in ELSEVIER: Applied Soft Computing, Volume 12, Issue 2, Iran, 2012, pp. 819–825.

    Google Scholar 

  2. Engelbrecht, Andries P. “Fundamentals Of Computational Swarm Intelligence”, University Of Pretoria, South Africa.

    Google Scholar 

  3. Hassanzadeh, H.R.; Rouhani, M. “A Multi-objective Gravitational Search Algorithm”, in IEEE: Second International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN), Liverpool, 2010, pp. 7–12.

    Google Scholar 

  4. Hatamlou, A.; Abdullah, S.; Othman, Z. “Gravitational search algorithm with heuristic search for clustering problems”, in IEEE: 3rd Conference on Data Mining and Optimization (DMO), Putrajaya, 2011, pp. 190–193.

    Google Scholar 

  5. Holliday, D., Resnick, R., Walker, J., Fundamental of physic, John Wiley & Son, 1993.

    Google Scholar 

  6. Kennedy, J., and R. C. Eberhart. 2001. Swarm Intelligence. San Francisco: Morgan Kaufmann.

    Google Scholar 

  7. Mirjalili, S.; Hashim, S.Z.M. “A new hybrid PSOGSA algorithm for function optimization”, in IEEE: International Conference on Computer and Information Application (ICCIA), Tianjin, 2010, pp. 374–377.

    Google Scholar 

  8. Mirjalili, S.; Hashim, S.; Sardroudi, H. “Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm”, in ELSEVIER: Applied Mathematics and Computation, Volume 218, Issue 22, Malaysia, 2012, pp. 11125–11137.

    Google Scholar 

  9. Olivas, F., Valdez, F., & Castillo, O. (2014). A Comparative Study of Membership Functions for an Interval Type-2 Fuzzy System used to Dynamic Parameter Adaptation in Particle Swarm Optimization. In Recent Advances on Hybrid Approaches for Designing Intelligent Systems (pp. 67–78). Springer International Publishing.

    Google Scholar 

  10. Pagnin,A.; Schellini,S.A.; Spadotto,A.; Guido,R.C.; Ponti,M.; Chiachia,G.; Falcao,A.X. “Feature selection through gravitational search algorithm”, in IEEE: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, 2011, pp. 2052–2055.

    Google Scholar 

  11. Rashedi, E.; Nezamabadi-pour, H.; Saryazdi, S. “GSA: A Gravitational Search Algorithm”, in ELSEVIER: Information Sciences, Volume 179, Issue 13, Iran, 2009, pp. 2232–2248.

    Google Scholar 

  12. Sombra, A., Valdez, F., Melin, P., & Castillo, O. (2013, June). A new gravitational search algorithm using fuzzy logic to parameter adaptation. In Evolutionary Computation (CEC), 2013 IEEE Congress on (pp. 1068–1074). IEEE.

    Google Scholar 

  13. Verma, O.P.,Sharma, R. “Newtonian Gravitational Edge Detection Using Gravitational Search Algorithm”, in IEEE: International Conference on Communication Systems and Network Technologies (CSNT), Rajkot, 2012, pp. 184–188.

    Google Scholar 

  14. Zadeh L. (1965) “Fuzzy sets”. Information & Control, 8, 338–353.

    Google Scholar 

  15. Zadeh L. (1988) “Fuzzy logic”. IEEE Computer Mag., vol. 1, pp. 83–93.

    Google Scholar 

  16. Zadeh L. (1975) “The concept of a linguistic variable and its application to approximate reasoning—I,” Inform. Sci., vol. 8, pp. 199–249.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, Mexico

    Frumen Olivas, Fevrier Valdez & Oscar Castillo

Authors
  1. Frumen Olivas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fevrier Valdez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Oscar Castillo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oscar Castillo .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, BC, Mexico

    Patricia Melin

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, BC, Mexico

    Oscar Castillo

  3. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Olivas, F., Valdez, F., Castillo, O. (2017). Gravitational Search Algorithm with Parameter Adaptation Through a Fuzzy Logic System. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Nature-Inspired Design of Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 667. Springer, Cham. https://doi.org/10.1007/978-3-319-47054-2_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-47054-2_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47053-5

  • Online ISBN: 978-3-319-47054-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation