Skip to main content

A Hybrid ICA/PSO Algorithm by Adding Independent Countries for Large Scale Global Optimization

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7198)

Abstract

This paper presents the hybrid approach of Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO) for global optimization. In standard ICA, there are only two types of countries: imperialists and colonies. In the proposed hybrid algorithm (ICA/PSO) we added another type of country, ‘Independent’. Independent countries do not fall into the category of empires, and are anti-imperialism. In addition, they are united and their shared goal is to get stronger in order to rescue colonies and help them join independent countries. These independent countries are aware of each other positions and make use of swarm intelligence in PSO for their own progress. Experimental results are examined with benchmark functions provided by CEC2010 Special Session on Large Scale Global Optimization (LSGO) and the results are compared with some previous LSGO algorithms, standard PSO and standard ICA.

Keywords

  • ICA
  • Global optimization
  • PSO
  • Hybrid evolutionary algorithm
  • Swarm intelligence

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

Buying options

Chapter
USD   29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (Canada)
  • Compact, lightweight 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Talbi, E.G.: Metaheuristic: from design to implementation. Wiley Publishing, Hoboken (2009)

    CrossRef  MATH  Google Scholar 

  2. Yang, X.S.: Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley Publishing, New Jersey (2010)

    CrossRef  Google Scholar 

  3. Yang, X.-S.: Metaheuristic Optimization: Algorithm Analysis and Open Problems. In: Pardalos, P.M., Rebennack, S. (eds.) SEA 2011. LNCS, vol. 6630, pp. 21–32. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  4. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, pp. 1942–1948 (1995)

    Google Scholar 

  5. Puranik, P., Bajaj, P., Abraham, A., Palsodkar, P., Deshmukh, A.: Human Perception-based Color Image Segmentation Using Comprehensive Learning Particle Swarm Optimization. Journal of Information Hiding and Multimedia Signal Processing 2(3), 227–235 (2011)

    CrossRef  Google Scholar 

  6. Chang, F.C., Huang, H.-C.: A Refactoring Method for Cache-Efficient Swarm Intelligence Algorithms. Information Sciences, doi:10.1016/j.ins.2010.02.025

    Google Scholar 

  7. Atashpaz-Gargari, E., Lucas, C.: Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Proceedings of the IEEE Congress on Evolutionary Computation, Singapore, pp. 4661–4667 (2007)

    Google Scholar 

  8. Nedjah, N., Mourelle, L.M.: Swarm Intelligent Systems. Springer, New York (2006)

    CrossRef  MATH  Google Scholar 

  9. Tang, K., et al.: Benchmark Functions for the CEC 2010 Special Session and Competition on Large-Scale Global Optimization: Nature Inspired Computation and Applications Laboratory Technical report (2010), http://nical.ustc.edu.cn/cec10ss.php

  10. Brest, J., Zamuda, A., Fister, I., Maučec, M.S.: Large Scale Global Optimization using Self-adaptive Differential Evolution Algorithm. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC). IEEE Press, Barcelona (2010)

    Google Scholar 

  11. Wang, H., Wu, Z., Rahnamayan, S., Jiang, D.: Sequential DE Enhanced by Neighborhood Search for Large Scale Global Optimization. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC). IEEE Press, Barcelona (2010)

    Google Scholar 

  12. Omidvar, M.N., Li, X., Yao, X.: Cooperative Co-evolution with Delta Grouping for Large Scale Non-separable Function Optimization. In: Proceedings of IEEE Congress onEvolutionary Computation (CEC), pp. 1762–1769. IEEE Press, Barcelona (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ghodrati, A., Malakooti, M.V., Soleimani, M. (2012). A Hybrid ICA/PSO Algorithm by Adding Independent Countries for Large Scale Global Optimization. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Intelligent Information and Database Systems. ACIIDS 2012. Lecture Notes in Computer Science(), vol 7198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28493-9_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28493-9_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28492-2

  • Online ISBN: 978-3-642-28493-9

  • eBook Packages: Computer ScienceComputer Science (R0)