Skip to main content
SpringerLink
Log in
Book cover

Proceedings of Sixth International Conference on Soft Computing for Problem Solving pp 1–11Cite as

Adaptive Scale Factor Based Differential Evolution Algorithm

  • Conference paper
  • First Online:

  • 614 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 546))

Abstract

In DE, exploration and exploitation capabilities depend on two processes, namely mutation and crossover. In these two processes exploration capability and exploitation capability is balanced using the tuning of scale factor F and crossover probability CR. In DE, for a high value of CR and F, there is always enough chance to skip the true solution due to large step size in the solution search space. Therefore in this article, a self-adaptive scale factor strategy is proposed in which scale factor is adaptively decided through iterations. In the proposed strategy, in the early iteration, the value of F is kept high to keep the large step size while in later iterations the value of F is kept small to keep the step size short. The proposed strategy is named as Adaptive Scale Factor based Differential Evolution (ASFDE) Algorithm. Further, to increase the exploration capability of the algorithm, a limit is associated with every solution to count the number of not updating iterations. If this count crosses the pre-defined limit, then the solution is randomly initialized. The proposed algorithm is tested over 12 different benchmark functions and correlate with standard DE, and another swarm intelligence based algorithm, namely artificial bee colony (ABC) algorithm, and particle swam optimization (PSO) algorithm. The obtained results reveal that ASFDE is a competitive variant of DE.

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bansal, J.C., Sharma, H., Arya, K.V., Nagar, A.: Memetic search in artificial bee colony algorithm. Soft Comput. 17(10), 1911–1928 (2013)

    Article  Google Scholar 

  2. Das, S., Mullick, S.S., Suganthan, P.N.: Recent advances in differential evolution-an updated survey. Swarm Evol. Comput. 27, 1–30 (2016)

    Article  Google Scholar 

  3. Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)

    Article  Google Scholar 

  4. Eberhart, R.C., Kennedy, J., et al.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, New York, NY, vol. 1, pp. 39–43 (1995)

    Google Scholar 

  5. Engelbrecht, A.P.: Computational Intelligence: An Introduction. Wiley, New York (2007)

    Book  Google Scholar 

  6. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  7. Lampinen, J., Zelinka, I.: On stagnation of the differential evolution algorithm (2000)

    Google Scholar 

  8. Neri, F., Tirronen, V.: Scale factor local search in differential evolution. Memetic Comput. 1(2), 153–171 (2009)

    Article  Google Scholar 

  9. Panigrahi, B.K., Suganthan, P.N., Das, S.: Swarm, Evolutionary, and Memetic Computing. LNCS, vol. 8947. Springer, Cham (2015)

    Book  MATH  Google Scholar 

  10. Price, K.V.: Differential evolution: a fast and simple numerical optimizer. In: 1996 Biennial Conference of the North American Fuzzy Information Processing Society, NAFIPS 1996, pp. 524–527. IEEE (1996)

    Google Scholar 

  11. Storn, R., Price, K.: Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces, vol. 3. ICSI, Berkeley (1995)

    Google Scholar 

  12. Yan, J.-Y., Ling, Q., Sun, D.: A differential evolution with simulated annealing updating method. In: 2006 International Conference on Machine Learning and Cybernetics, pp. 2103–2106. IEEE (2006)

    Google Scholar 

  13. Yang, X.-S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Bristol (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Rajasthan Technical University, Kota, India

    Nikky Choudhary, Harish Sharma & Nirmala Sharma

Authors
  1. Nikky Choudhary
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Harish Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nirmala Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nikky Choudhary .

Editor information

Editors and Affiliations

  1. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, India

    Kusum Deep

  2. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  3. Department of Mathematics, National Institute of Technology, Silchar, Silchar, Assam, India

    Kedar Nath Das

  4. School of Mathematics, Thapar Institute of Engineering and Technology University, Patiala, Punjab, India

    Arvind Kumar Lal

  5. School of Mathematics, Thapar University Patiala, Patiala, Punjab, India

    Harish Garg

  6. Department of Mathematics and Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya K. Nagar

  7. Department of Applied Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India

    Millie Pant

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Choudhary, N., Sharma, H., Sharma, N. (2017). Adaptive Scale Factor Based Differential Evolution Algorithm. In: Deep, K., et al. Proceedings of Sixth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 546. Springer, Singapore. https://doi.org/10.1007/978-981-10-3322-3_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-3322-3_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3321-6

  • Online ISBN: 978-981-10-3322-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation