Skip to main content
SpringerLink
Log in

Scaling up Multi-island Competitive Cooperative Coevolution for Real Parameter Global Optimisation

  • Conference paper
  • First Online:
AI 2015: Advances in Artificial Intelligence (AI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9457))

Included in the following conference series:

Abstract

A major challenge in using cooperative coevolution (CC) for global optimisation is the decomposition of a given problem into subcomponents. Variable interaction is a major constraint that determines the decomposition strategy of a problem. Hence, finding an optimal decomposition strategy becomes a burdensome task as inter-dependencies between decision variables are unknown for these problems. In recent related work, a multi-island competitive cooperative coevolution (MICCC) algorithm was introduced which featured competition and collaboration of several different decomposition strategies. MICCC used five different uniform problem decomposition strategies that were implemented as independent islands. This paper presents an analysis of the MICCC algorithm and also extends it to more than five islands. We incorporate arbitrary (non-uniform) problem decomposition strategies as additional islands in MICCC and monitor how each different problem decomposition strategy contributes towards the global fitness over different stages of optimisation.

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 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

References

  1. Potter, M.A., De Jong, K.A.: 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 

  2. Yang, Z., Tang, K., Yao, X.: Large scale evolutionary optimization using cooperative coevolution. Inf. Sci. 178(15), 2985–2999 (2008)

    Article  MathSciNet  MATH  Google Scholar 

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

    MATH  Google Scholar 

  4. 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 

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

    Article  Google Scholar 

  6. 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, IEEE 2001, vol. 2, pp. 1101–1108 (2001)

    Google Scholar 

  7. Mahdavi, S., Shiri, M.E., Rahnamayan, S.: Cooperative co-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 

  8. 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 

  9. 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 on Evolutionary Computation, pp. 1762–1769 (2010)

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  12. Bali, K., Chandra, R.: Multi-island competitive cooperative coevolution for real parameter global optimization. In: International Conference on Neural Information Processing (ICONIP), Istanbul, Turkey, November 2015 (in press)

    Google Scholar 

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

    Google Scholar 

  14. Bali, K., Chandra, R., Omidvar, M.N.: Competitive island-based cooperative co-evolution for efficient optimization of large-scale fully-separable continuous functions. In: International Conference on Neural Information Processing (ICONIP), Istanbul, Turkey, November 2015 (in press)

    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. 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 

  18. 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 

  19. 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. Technical report, Nature Inspired Computation and Applications Laboratory, USTC, China (2007). http://nical.ustc.edu.cn/cec08ss.php

  20. Li, X., Tang, K., Omidvar, M.N., Yang, Z., Qin, K.: Benchmark functions for the CEC 2013 special session and competition on large-scale global optimization. Technical report, RMIT University, Melbourne, Australia (2013). http://goanna.cs.rmit.edu.au/xiaodong/cec13-lsgo

  21. 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. Last accessed July 2010

    Google Scholar 

  22. Omidvar, M.N., Li, X., Yao, X.: Smart use of computational resources based on contribution for cooperative co-evolutionary algorithms. In: Proceedings of Genetic and Evolutionary Computation Conference, pp. 1115–1122. ACM (2011)

    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. The University of Waikato, Hamilton, New Zealand

    Bernhard Pfahringer

  2. The Australian National University, Canberra, Aust Capital Terr, Australia

    Jochen Renz

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). Scaling up Multi-island Competitive Cooperative Coevolution for Real Parameter Global Optimisation. In: Pfahringer, B., Renz, J. (eds) AI 2015: Advances in Artificial Intelligence. AI 2015. Lecture Notes in Computer Science(), vol 9457. Springer, Cham. https://doi.org/10.1007/978-3-319-26350-2_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-26350-2_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26349-6

  • Online ISBN: 978-3-319-26350-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation