Skip to main content

RWS-L-SHADE: An Effective L-SHADE Algorithm Incorporation Roulette Wheel Selection Strategy for Numerical Optimisation

  • Conference paper
  • First Online:
Applications of Evolutionary Computation (EvoApplications 2022)

Abstract

Differential evolution (DE) is widely used for global optimisation problems due to its simplicity and efficiency. L-SHADE is a state-of-the-art variant of DE algorithm that incorporates external archive, success-history-based parameter adaptation, and linear population size reduction. L-SHADE uses a current-to-pbest/1/bin strategy for mutation operator, while all individuals have the same probability to be selected. In this paper, we propose a novel L-SHADE algorithm, RWS-L-SHADE, based on a roulette wheel selection strategy so that better individuals have a higher priority and worse individuals are less likely to be selected. Our extensive experiments on the CEC-2017 benchmark functions and dimensionalities of 30, 50 and 100 indicate that RWS-L-SHADE outperforms L-SHADE.

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

Access this chapter

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Awad, N.H., Ali, M.Z., Suganthan, P.N., Reynolds, R.G.: Differential evolution-based neural network training incorporating a centroid-based strategy and dynamic opposition-based learning. In: IEEE Congress on Evolutionary Computation, pp. 2958–2965. IEEE (2016)

    Google Scholar 

  2. Cai, Z., Gong, W., Ling, C.X., Zhang, H.: A clustering-based differential evolution for global optimization. Appl. Soft Comput. 11(1), 1363–1379 (2011)

    Article  Google Scholar 

  3. Fister, I., Fister, D., Deb, S., Mlakar, U., Brest, J., Fister, I.: Post hoc analysis of sport performance with differential evolution. Neural Comput. Appl. 32(15), 10799–10808 (2018). https://doi.org/10.1007/s00521-018-3395-3

    Article  Google Scholar 

  4. Liang, J., Qu, B., Suganthan, P.: Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore 635 (2013)

    Google Scholar 

  5. Mohamed, A.W., Almazyad, A.S.: Differential evolution with novel mutation and adaptive crossover strategies for solving large scale global optimization problems. Appl. Comput. Intell. Soft Comput. 2017 (2017)

    Google Scholar 

  6. Mousavirad, S.J., Ebrahimpour-Komleh, H.: Human mental search: a new population-based metaheuristic optimization algorithm. Appl. Intell. 47(3), 850–887 (2017). https://doi.org/10.1007/s10489-017-0903-6

    Article  Google Scholar 

  7. Mousavirad, S.J., Rahnamayan, S.: Differential evolution algorithm based on a competition scheme. In: 14th International Conference on Computer Science and Education (2019)

    Google Scholar 

  8. Mousavirad, S.J., Rahnamayan, S.: Enhancing SHADE and L-SHADE algorithms using ordered mutation. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 337–344. IEEE (2020)

    Google Scholar 

  9. Mousavirad, S.J., Rahnamayan, S.: Evolving feedforward neural networks using a quasi-opposition-based differential evolution for data classification. In: IEEE Symposium Series on Computational Intelligence (2020)

    Google Scholar 

  10. Mousavirad, S.J., Rahnamayan, S.: A novel center-based differential evolution algorithm. In: Congress on Evolutionary Computation. IEEE (2020)

    Google Scholar 

  11. Mousavirad, S.J., Rahnamayan, S.: One-array differential evolution algorithm with a novel replacement strategy for numerical optimization. In: International Conference on Systems, Man, and Cybernetics (2020)

    Google Scholar 

  12. Mousavirad, S.J., Rahnamayan, S., Schaefer, G.: Many-level image thresholding using a center-based differential evolution algorithm. In: Congress on Evolutionary Computation (2020)

    Google Scholar 

  13. Mousavirad, S.J., Schaefer, G., Korovin, I.: A global-best guided human mental search algorithm with random clustering strategy. In: International Conference on Systems, Man and Cybernetics, pp. 3174–3179 (2019)

    Google Scholar 

  14. Mousavirad, S.J., Schaefer, G., Korovin, I., Oliva, D.: RDE-OP: a region-based differential evolution algorithm incorporation opposition-based learning for optimising the learning process of multi-layer neural networks. In: 24th International Conference on the Applications of Evolutionary Computation (2021)

    Google Scholar 

  15. Mousavirad, S.J., Zabihzadeh, D., Oliva, D., Perez-Cisneros, M., Schaefer, G.: A grouping differential evolution algorithm boosted by attraction and repulsion strategies for masi entropy-based multi-level image segmentation. Entropy 24(1), 8 (2022)

    Article  Google Scholar 

  16. Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.: Opposition-based differential evolution. IEEE Trans. Evol. Comput. 12(1), 64–79 (2008)

    Article  Google Scholar 

  17. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: IEEE International Conference on Evolutionary Computation, pp. 69–73 (1998)

    Google Scholar 

  18. Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  Google Scholar 

  19. Tanabe, R., Fukunaga, A.: Success-history based parameter adaptation for differential evolution. In: IEEE Congress on Evolutionary Computation, pp. 71–78. IEEE (2013)

    Google Scholar 

  20. Tanabe, R., Fukunaga, A.S.: Improving the search performance of shade using linear population size reduction. In: IEEE Congress on Evolutionary Computation, pp. 1658–1665. IEEE (2014)

    Google Scholar 

  21. Wang, X., et al.: Massive expansion and differential evolution of small heat shock proteins with wheat (triticum aestivum l.) polyploidization. Sci. Rep. 7(1), 1–12 (2017)

    Google Scholar 

  22. Wu, G., Mallipeddi, R., Suganthan, P.: Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization. Technical report, Nanyang Technological University, Singapore (2016)

    Google Scholar 

  23. Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Seyed Jalaleddin Mousavirad .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mousavirad, S.J., Moghadam, M.H., Saadatmand, M., Chakrabortty, R., Schaefer, G., Oliva, D. (2022). RWS-L-SHADE: An Effective L-SHADE Algorithm Incorporation Roulette Wheel Selection Strategy for Numerical Optimisation. In: Jiménez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds) Applications of Evolutionary Computation. EvoApplications 2022. Lecture Notes in Computer Science, vol 13224. Springer, Cham. https://doi.org/10.1007/978-3-031-02462-7_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-02462-7_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-02461-0

  • Online ISBN: 978-3-031-02462-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics