Skip to main content
SpringerLink
Log in

Differential Evolution Algorithm with Fine Evaluation Strategy for Multi-dimensional Function Optimization Problems

  • Conference paper
Artificial Intelligence and Computational Intelligence (AICI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7002))

  • 2329 Accesses

Abstract

For multi-dimensional function optimization problems, classical differential evolution (DE) algorithm may deteriorate its intensification ability because different dimensions may interfere with each other. To deal with this intrinsic shortage, this paper presents a DE algorithm framework with fine evaluation strategy. In the process of search, solution is updated and evaluated dimension by dimension. In each dimension, the updated value will be accepted only if it can improve the solution. In case that there is no improvement found in any dimension, the new solution, which is calculated using classical mutation operator only, will be accepted in low probability. This strategy can improve diversification and keep DE algorithm from premature convergence. Simulation experiments were carried on typical benchmark functions, and the results show that fine evaluation strategy can improve the performance of DE algorithm remarkably.

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Storn, R., Price, K.: Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces. Journal of Global Optimization 11, 341–359 (2028), doi:10.1023/A:1008202821328

    Article  MathSciNet  MATH  Google Scholar 

  2. Neri, F., Tirronen, V.: Recent advances in differential evolution: a survey and experimental analysis. Artificial Intelligence Review 33, 61–106 (2010), doi:10.1007/s10462-009-9137-2

    Article  Google Scholar 

  3. Ali1, M., Pant1, M., Abraham, A.: Simplex Differential Evolution. Acta Polytechnica Hungarica 6(5), 95–115 (2009)

    Google Scholar 

  4. Fan, H.Y., Lampinen, J.: A trigonometric mutation operation to differential evolution. Journal of Global Optimization 27, 105–129 (2003), doi:10.1023/A:1024653025686

    Article  MathSciNet  MATH  Google Scholar 

  5. Hu, S., Huang, H., Czarkowski, D.: Hybrid trigonometric differential evolution for optimizing harmonic distribution. In: IEEE International Symposium on Circuits and Systems, vol. 2, pp. 1306–1309 (May 2005), doi:10.1109/ISCAS.2005.1464835

    Google Scholar 

  6. Angira, R., Santosha, A.: Optimization of dynamic systems: a trigonometric differential evolution approach. Computer & Chemical Engneering 31, 1055–1063 (2007), doi:10.1016/j.compchemeng.2006.09.015

    Article  Google Scholar 

  7. Angira, R., Santosh, A.: A modified trigonometric differential evolution algorithm for optimization of dynamic systems. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1463–1468 (June 2008), doi:10.1109/CEC.2008.4630986

    Google Scholar 

  8. Noman, N., Iba, H.: Enhancing differential evolution performance with local search for high dimensional function optimization. In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, pp. 967–974 (June 2005), doi:10.1145/1068009.1068174

    Google Scholar 

  9. Noman, N., Iba, H.: Accelerating differential evolution using an adaptive local search. IEEE Transaction on Evolutionary Computation 12, 107–125 (2008), doi:10.1109/TEVC.2007.895272

    Article  Google Scholar 

  10. Brest, J., Maǔcec, M.S.: Population size reduction for the differential evolution algorithm. Appllied Intelligence 29, 228–247 (2008), doi:10.1007/s10489-007-0091-x

    Google Scholar 

  11. Brest, J., Zamuda, A., Boškovíc, B., Maucec, M.S., Žumer, V.: High-dimensional real-parameter optimization using self-adaptive differential evolution algorithm with population size reduction. In: IEEE World Congress on Computational Intelligence, pp. 2032–2039 (June 2008), doi:10.1109/CEC.2008.4631067

    Google Scholar 

  12. Neri, F., Tirrone, V.: Scale factor local search in differential evolution. Memetic Computing 1, 153–171 (2009), doi:10.1007/s12293-009-0008-9

    Article  Google Scholar 

  13. Tirronen, V., Neri, F., Rossi, T.: Enhancing differential evolution frameworks by scale factor local search—part I. In: IEEE Congress on Evolutionary Computation, pp. 94–101 (May 2009), doi:10.1109/CEC.2009.4982935

    Google Scholar 

  14. Neri, F., Tirronen, V., Kärkkäinen, T.: Enhancing differential evolution frameworks by scale factor local search—part II. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 118–125 (May 2009), doi:10.1109/CEC.2009.4982938

    Google Scholar 

  15. Brest, J., Žumer, V., Maucec, M.: Self-adaptive differential evolution algorithm in constrained real-parameter optimization. In: IEEE Congress on Evolutionary Computation, pp. 215–222 (December 2006), doi:10.1109/CEC.2006.1688311

    Google Scholar 

  16. Brest, J., Greiner, S., Boškovíc, B., Mernik, M., Žumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10, 646–657 (2006), doi:10.1109/TEVC.2006.872133.

    Article  Google Scholar 

  17. Zamuda, A., Brest, J., Boškovíc, B., Žumer, V.: Differential evolution for multiobjective optimization with self adaptation. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 3617–3624 (September 2007), doi:10.1109/CEC.2007.4424941

    Google Scholar 

  18. Brest, J., Zamuda, A., Žumer, V.: An analysis of the control parameters’adaptation in DE. In: Chakraborty, U.K. (ed.) Advances In Differential Evolution, vol. 143, pp. 89–110 (July 2008), doi:10.1007/978-3-540-68830-3_3

    Google Scholar 

  19. Rahnamayan, S., Tizhoosh, H., Salama, M.M.A.: Opposition-based differential evolution for optimization of noisy problems. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1865–1872 (September 2006), doi:10.1109/CEC.2006.1688534

    Google Scholar 

  20. Rahnamayan, S., Tizhoosh, H., Salama, M.M.A.: Quasi-oppositional differential evolution. In: Proceedings of the IEEE Congress On Evolutionary Computation, pp. 2229–2236 (September 2007), doi:10.1109/CEC.2007.4424748

    Google Scholar 

  21. Rahnamayan, S., Tizhoosh, H., Salama, M.M.A.: Opposition-based differential evolution. In: IEEE Transaction on Evolutinary Computation, vol. 12, pp. 64–79 (February 12, 2008), doi:10.1109/TEVC.2007.894200

    Google Scholar 

  22. Chakraborty, U.K., Das, S., Konar, A.: Differential evolution with local neighborhood. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 2042–2049 (September 2006), doi:10.1109/CEC.2006.1688558

    Google Scholar 

  23. Das, S., Abraham, A., Chakraborty, U.K., Konar, A.: Differential evolution with a neighborhood-based mutation operator. IEEE Transaction on Evolutionay Computation 13, 526–553 (2009), doi:10.1109/TEVC.2008.2009457

    Article  Google Scholar 

  24. Qin, A.K., Suganthan, P.N.: Self-adaptive differential evolution algorithm for numerical optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 2, pp. 1785–1791 (September 2005), doi:10.1109/CEC.2005.1554904

    Google Scholar 

  25. Lampinen, J., Zelinka, I.: On stagnation of the differential evolution algorithm. In: Proceedings of 6th International Mendel Conference on Soft Computing, pp. 76–83 (June 2000), doi: 10.1.1.35.7932

    Google Scholar 

  26. Zielinski, K., Wang, X., Laur, R.: Comparison of adaptive approaches for differential evolution. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 641–650. Springer, Heidelberg (2008), doi:10.1007/978-3-540-87700-4-64

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, China

    Xiaoyu Lin, Lijin Wang & Yiwen Zhong

  2. Pervasive Technology Institute, Indiana University, Indianapolis, USA

    Hui Zhang

Authors
  1. Xiaoyu Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lijin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yiwen Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Business Information Technology, RMIT University, City Campus, 124 La Trobe Street, 3000, Melbourne, VIC, Australia

    Hepu Deng

  2. School of Electronics and Information, Tongji University, 201804, Shanghai, China

    Duoqian Miao

  3. School of Computer and Information Engineering, Shanghai University of Electric Power, 200090, Shanghai, China

    Jingsheng Lei

  4. Department of Business Administration, Caritas Institute of Higher Education, 18 Chui Ling Road, Tseung Kwan O, Hong Kong, China

    Fu Lee Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lin, X., Wang, L., Zhong, Y., Zhang, H. (2011). Differential Evolution Algorithm with Fine Evaluation Strategy for Multi-dimensional Function Optimization Problems. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7002. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23881-9_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-23881-9_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23880-2

  • Online ISBN: 978-3-642-23881-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation