Skip to main content
SpringerLink
Log in
Book cover

Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization pp 219–232Cite as

Imperialist Competitive Algorithm Applied to the Optimization of Mathematical Functions: A Parameter Variation Study

  • Chapter
  • First Online:

Part of the book series: Studies in Computational Intelligence ((SCI,volume 601))

Abstract

This paper applies the imperialist competitive algorithm (ICA) to benchmark mathematical functions with the original method to analyze and perform a study of the variation of the results obtained with the ICA algorithm as we vary the parameters manually for 4 mathematical functions. The results demonstrate the efficiency of the algorithm to optimization problems and give us the pattern for future work in dynamically adapting these parameters.

This is a preview of subscription content, 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   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
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

Learn about institutional subscriptions

References

  1. Atashpaz-Gargari, E., Lucas, Y.C.: Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Evolutionary Computation, pp. 4661–4667 (2007)

    Google Scholar 

  2. Banaei, M., Seyed-Shenava, S., Farahbakhsh, Y.P.: Dynamic stability enhancement of power system based on a typical unified power flow controllers using imperialist competitive algorithm. Ain Shams Eng. J. 5(3), 691–702 (2014)

    Google Scholar 

  3. Dallegrave Afonso, L., Cocco Mariani, V., dos Santos Coelho, Y.L.: Modified imperialist competitive algorithm based on attraction and repulsion concepts for reliability-redundancy optimization. Expert Syst. Appl. 40(9), 3794–3802 (2013)

    Google Scholar 

  4. Duan, H., Huang, Y.L.Z.: Imperialist competitive algorithm optimized artificial neural networks for UCAV global path planning. Neurocomputing 125, 166–171 (2013)

    Google Scholar 

  5. Goldansaz, S.M., Fariborz, J., Zahedi Anaraki, Y.A.H.: A hybrid imperialist competitive algorithm for minimizing makespan in a multi-processor open shop. Appl. Math. Model. 37(23), 9603–9616 (2013)

    Google Scholar 

  6. Hosseini, S.M., Al Khaled, Y.A.: A survey on the imperialist competitive algorithm metaheuristic: implementation in engineering domain and directions for future research. Appl. Soft Comput. J. 24, 1078–1094 (2014)

    Google Scholar 

  7. Jula, A., Othman, Z., Sundararajan, Y.E.: Imperialist competitive algorithm with PROCLUS classifier for service time optimization in cloud computing service composition. Expert Syst. Appl. 42(1), 135–145 (2014)

    Google Scholar 

  8. Melin, P., Olivas, F., Castillo, O., Valdez, F., Soria, J., Valdez, M.: Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic. Expert Syst. Appl. 40(8), 3196–3206 (2012)

    Article  Google Scholar 

  9. Mozafaria, A., Behzad, Y., Ayoba, A.: Optimization of adhesive-bonded fiber glass strip using imperialist competitive algorithm. Proc. Technol. 1, 194–198 (2012)

    Google Scholar 

  10. Nourmohammadia, A., Zandiehb, M., Tavakkoli-Moghaddamca, Y.R.: An imperialist competitive algorithm for multi-objective U-type assembly line design. J. Comput. Sci. 4(5), 393–400 (2012)

    Google Scholar 

  11. Rasul, E., Javedani Sadaei, H., Abdullah, A.H., Gani, Y.A.: Imperialist competitive algorithm combined with refined high-order weighted fuzzy time series (RHWFTS–ICA) for short term load forecasting. Energy Convers. Manage. 76, 1104–1116 (2013)

    Google Scholar 

  12. Shamshirband, S., Amini, A., Nor Badrul, A., Mat Kiah, M.L., Ying, W.T., Furnell, Y.S.: D-FICCA: a density-based fuzzy imperialist competitive clustering algorithm for intrusion detection in wireless sensor networks. J. Int. Measur. Confederation 55, 212–226 (2014)

    Google Scholar 

Download references

Acknowledgments

We would like to express our gratitude to the CONACYT and Tijuana Institute of Technology for the facilities and resources granted for the development of this research.

Author information

Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, BC, México

    Emer Bernal, Oscar Castillo & José Soria

Authors
  1. Emer Bernal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oscar Castillo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. José Soria
    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, Baja California, Mexico

    Patricia Melin

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

    Oscar Castillo

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

    Janusz Kacprzyk

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Bernal, E., Castillo, O., Soria, J. (2015). Imperialist Competitive Algorithm Applied to the Optimization of Mathematical Functions: A Parameter Variation Study. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization. Studies in Computational Intelligence, vol 601. Springer, Cham. https://doi.org/10.1007/978-3-319-17747-2_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-17747-2_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17746-5

  • Online ISBN: 978-3-319-17747-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation