Skip to main content
SpringerLink
Log in

A Hybrid of Artificial Electric Field Algorithm and Differential Evolution for Continuous Optimization Problems

  • Conference paper
  • First Online:
Proceedings of 7th International Conference on Harmony Search, Soft Computing and Applications

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 140))

Abstract

The artificial electric field algorithm (AEFA) is a minimum and maximum value charged-based function optimization scheme that was created for continuous optimization problems in the first place. The deep-rooted AEFA is always one of the best-designed techniques for discovering optimal solutions to real-world optimization problems. Differential evolution is one of the best established optimization algorithm in the literature. This article aims to utilize the potential of AEFA and DE together to produce an efficient hybrid. Both AEFA and DE are synchronized to sum their strengths. The performance of the proposed hybrid algorithm is validated on seventeen benchmark problems including IEEE-CEC 2019 single objective unconstrained optimization problems and obtained experimental results are compared with several optimization algorithms. We used a statistical test, the Wilcoxon signed-rank test, to assess the randomness of the results produced by the suggested AEFA-DE. The experimental results suggest that the AEFA-DE has superior performance than others.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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. Ali MZ, Suganthan PN, Price KV, Awad NH (2019) The 2019 100-digit challenge on real-parameter, single objective optimization: analysis of results

    Google Scholar 

  2. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39

    Article  Google Scholar 

  3. Glover F, Laguna M (1998) Tabu search. In: Handbook of combinatorial optimization. Springer, pp 2093–2229

    Google Scholar 

  4. Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213(3):267–289

    Article  Google Scholar 

  5. Koza JR, Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection, vol 1. MIT Press

    Google Scholar 

  6. Liu J, Lampinen J (2005) A fuzzy adaptive differential evolution algorithm. Soft Comput 9(6):448–462

    Article  Google Scholar 

  7. Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073

    Article  MathSciNet  Google Scholar 

  8. Mirjalili S (2019) Genetic algorithm. In: Evolutionary algorithms and neural networks. Springer, pp 43–55

    Google Scholar 

  9. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Article  Google Scholar 

  10. Mohamed AW, Hadi AA, Fattouh AM, Jambi KM (2017) LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems. In: 2017 IEEE congress on evolutionary computation (CEC). IEEE, pp 145–152

    Google Scholar 

  11. Molga M, Smutnicki C (2005) Test functions for optimization needs, vol 101, p 48

    Google Scholar 

  12. Omran MG, Engelbrecht AP, Salman A (2007) Differential evolution based particle swarm optimization. In: 2007 IEEE swarm intelligence symposium. IEEE, pp 112–119

    Google Scholar 

  13. Omran MG, Salman A, Engelbrecht AP (2005) Self-adaptive differential evolution. In: International conference on computational and information science. Springer, pp 192–199

    Google Scholar 

  14. Pant M, Thangaraj R, Abraham A (2011) DE-PSO: a new hybrid meta-heuristic for solving global optimization problems. New Math Nat Comput 7(03):363–381

    Article  MathSciNet  Google Scholar 

  15. Pant M, Zaheer H, Garcia-Hernandez L, Abraham A et al (2020) Differential evolution: a review of more than two decades of research. Eng Appl Artif Intell 90:103479

    Article  Google Scholar 

  16. Price KV (2013) Differential evolution. In: Handbook of optimization. Springer, pp 187–214

    Google Scholar 

  17. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  Google Scholar 

  18. Stanovov V, Akhmedova S, Semenkin E (2018) LSHADE algorithm with rank-based selective pressure strategy for solving CEC 2017 benchmark problems. In: 2018 IEEE congress on evolutionary computation (CEC). IEEE, pp 1–8

    Google Scholar 

  19. Storn R (1996) On the usage of differential evolution for function optimization. In: Proceedings of North American fuzzy information processing. IEEE, pp 519–523

    Google Scholar 

  20. Tanabe R, Fukunaga A (2013) Success-history based parameter adaptation for differential evolution. In: 2013 IEEE congress on evolutionary computation. IEEE, pp 71–78

    Google Scholar 

  21. Tanabe R, Fukunaga AS (2014) Improving the search performance of shade using linear population size reduction. In: 2014 IEEE congress on evolutionary computation (CEC). IEEE, pp 1658–1665

    Google Scholar 

  22. Van Laarhoven PJ, Aarts EH (1987) Simulated annealing. In: Simulated annealing: theory and applications. Springer, pp 7–15

    Google Scholar 

  23. Wilcoxon F (1992) Individual comparisons by ranking methods. In: Breakthroughs in statistics. Springer, pp 196–202

    Google Scholar 

  24. Yadav A et al (2019) AEFA: artificial electric field algorithm for global optimization. Swarm Evol Comput 48:93–108

    Article  Google Scholar 

  25. Yang X-S, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput

    Google Scholar 

  26. Zhang C, Ning J, Lu S, Ouyang D, Ding T (2009) A novel hybrid differential evolution and particle swarm optimization algorithm for unconstrained optimization. Oper Res Lett 37(2):117–122

    Article  MathSciNet  Google Scholar 

  27. Zhang W-J, Xie X-F (2003) DEPSO: hybrid particle swarm with differential evolution operator. In: SMC’03 conference proceedings. 2003 IEEE international conference on systems, man and cybernetics. Conference theme-system security and assurance (Cat. No. 03CH37483), vol 4. IEEE, pp 3816–3821

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics Dr. B.R. Ambedkar National Institute of Technology Jalandhar, Jalandhar, Punjab, 144011, India

    Dikshit Chauhan & Anupam Yadav

Authors
  1. Dikshit Chauhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anupam Yadav
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dikshit Chauhan .

Editor information

Editors and Affiliations

  1. School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

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

    Kusum Deep

  3. Department of Energy IT, Gachon University, Seongnam, Korea (Republic of)

    Zong Woo Geem

  4. School of Mechanical Engineering, University of Science and Culture, Tehran, Iran

    Ali Sadollah

  5. Department of Mathematics, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India

    Anupam Yadav

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chauhan, D., Yadav, A. (2022). A Hybrid of Artificial Electric Field Algorithm and Differential Evolution for Continuous Optimization Problems. In: Kim, J.H., Deep, K., Geem, Z.W., Sadollah, A., Yadav, A. (eds) Proceedings of 7th International Conference on Harmony Search, Soft Computing and Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 140. Springer, Singapore. https://doi.org/10.1007/978-981-19-2948-9_49

Download citation

Publish with us

Policies and ethics

Search

Navigation