Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Multi-Island Competitive Cooperative Coevolution for Real Parameter Global Optimization

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9491))

Included in the following conference series:

  • 2800 Accesses

Abstract

Problem decomposition is an important attribute of cooperative coevolution that depends on the nature of the problems in terms of separability which is defined by the level of interaction amongst decision variables. Recent work in cooperative coevolution featured competition and collaboration of problem decomposition methods that was implemented as islands in a method known as competitive island cooperative coevolution (CICC). In this paper, a multi-island competitive cooperative coevolution algorithm (MICCC) is proposed in which several different problem decomposition strategies are given a chance to compete, collaborate and motivate other islands while converging to a common solution. The performance of MICCC is evaluated on eight different benchmark functions and are compared with CICC where only two islands were utilized. The results from the experimental analysis show that competition and collaboration of several different island can yield solutions with a quality better than the two-island competition algorithm (CICC) on most complex multi-modal problems.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • 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

Institutional subscriptions

Similar content being viewed by others

References

  1. Bäck, T., Fogel, D.B., Michalewicz, Z. (eds.): Handbook of Evolutionary Computation. Institute of Physics Publishing, Bristol, and Oxford University Press, New York (1997)

    Google Scholar 

  2. Potter, M.A., De Jong, K.: A cooperative coevolutionary approach to function optimization. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994)

    Chapter  Google Scholar 

  3. Chandra, R., Frean, M., Zhang, M.: On the issue of separability for problem decomposition in cooperative neuro-evolution. Neurocomputing 87, 33–40 (2012)

    Article  Google Scholar 

  4. Omidvar, M.N., Mei, Y., Li, X.: Effective decomposition of large-scale separable continuous functions for cooperative co-evolutionary algorithms. In: Proceeding of IEEE Congress on Evolutionary Computation, pp. 1305–1312 (2014)

    Google Scholar 

  5. Salomon, R.: Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions - a survey of some theoretical and practical aspects of genetic algorithms. Biosyst. 39, 263–278 (1995)

    Article  Google Scholar 

  6. Omidvar, M., Li, X., Mei, Y., Yao, X.: Cooperative co-evolution with differential grouping for large scale optimization. IEEE Trans. Evol. Comput. 18(3), 378–393 (2014)

    Article  Google Scholar 

  7. Liu, Y., Yao, X., Zhao, Q., Higuchi, T.: Scaling up fast evolutionary programming with cooperative coevolution. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 2. IEEE, pp. 1101–1108 (2001)

    Google Scholar 

  8. Potter, M.A., De Jong, K.: A cooperative coevolutionary approach to function optimization. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994)

    Chapter  Google Scholar 

  9. Salomon, R.: Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. a survey of some theoretical and practical aspects of genetic algorithms. BioSystems 39(3), 263–278 (1996)

    Article  Google Scholar 

  10. Li, X., Tang, K., Omidvar, M.N., Yang, Z., Qin, K., China, H.: Benchmark functions for the CEC 2013 special session and competition on large-scale global optimization. gene 7(33), 8 (2013)

    Google Scholar 

  11. Mahdavi, S., Shiri, M.E., Rahnamayan, S.: Cooperativeco-evolution with a new decomposition method for large-scale optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2014, pp. 1285–1292 (2014)

    Google Scholar 

  12. Chen, W., Weise, T., Yang, Z., Tang, K.: Large-scale global optimization using cooperative coevolution with variable interaction learning. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6239, pp. 300–309. Springer, Heidelberg (2010)

    Google Scholar 

  13. Omidvar, M.N., Li, X., Yao, X.: Cooperative co-evolution with delta grouping for large scale non-separable function optimization. In: Proceeding of IEEE Congress on Evolutionary Computation, pp. 1762–1769 (2010)

    Google Scholar 

  14. Omidvar, M.N., Li, X., Tang, K.: Designing benchmark problems for large-scale continuous optimization. Inf. Sci. 316, 419–436 (2015)

    Article  Google Scholar 

  15. Chandra, R.: Competition and collaboration in cooperative coevolution of Elman recurrent neural networks for time-series prediction. IEEE Trans. Neural Netw. Learn. Syst. (2015). doi:10.1109/TNNLS.2015.2404823. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7055352&isnumber=6104215

  16. Chandra, R.: Competitive two-island cooperative coevolution for training Elman recurrent networks for time series prediction. In: International Joint Conference on Neural Networks (IJCNN), Beijing, China, pp. 565–572, July 2014

    Google Scholar 

  17. Chandra, R., Bali, K.: Competitive two island cooperative coevolution for real parameter global optimization. In: IEEE Congress on Evolutionary Computation, Sendai, Japan, pp. 93–100, May 2015

    Google Scholar 

  18. Li, W., Wang, L.: A competitive-cooperative co-evolutionary optimizationalgorithm based on cloud model. In: Fourth International Workshop on Advanced Computational Intelligence (IWACI 2011), pp. 662–669. IEEE (2011)

    Google Scholar 

  19. Deb, K., Anand, A., Joshi, D.: A computationally efficient evolutionary algorithm for real-parameter optimization. Evol. Comput. 10(4), 371–395 (2002)

    Article  Google Scholar 

  20. Van den Bergh, F., Engelbrecht, A.P.: A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 225–239 (2004)

    Article  Google Scholar 

  21. Omidvar, M.N., Mei, Y., Li, X.: Effective decomposition of large-scale separable continuous functions for cooperative co-evolutionary algorithms. In: IEEE Congress on Evolutionary Computation (CEC 2014), pp. 1305–1312. IEEE (2014)

    Google Scholar 

  22. Tang, K., Yao, X., Suganthan, P.N., MacNish, C., Chen, Y.P., Chen, C.M., Yang, Z.: Benchmark functions for the CEC’2008 special session and competition on large scale global optimization. Nature Inspired Computation and Applications Laboratory, USTC, China, Technical report (2007). http://nical.ustc.edu.cn/cec08ss.php

  23. Herrera, F., Lozano, M., Molina, D.: Test suite for the special issue of soft computing on scalability of evolutionary algorithms and other metaheuristics for large scale continuous optimization problems (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing Information and Mathematical Sciences, University of South Pacific, Suva, Fiji

    Kavitesh K. Bali & Rohitash Chandra

  2. Artificial Intelligence and Cybernetics Research Group, Software Foundation, Nausori, Fiji

    Kavitesh K. Bali & Rohitash Chandra

Authors
  1. Kavitesh K. Bali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rohitash Chandra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kavitesh K. Bali .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Bali, K.K., Chandra, R. (2015). Multi-Island Competitive Cooperative Coevolution for Real Parameter Global Optimization. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-26555-1_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26554-4

  • Online ISBN: 978-3-319-26555-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation