Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Distributed Computing and Artificial Intelligence

DCAI 2018: Distributed Computing and Artificial Intelligence, 15th International Conference pp 27–35Cite as

A Comparative Study of Transfer Functions in Binary Evolutionary Algorithms for Single Objective Optimization

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 800))

Abstract

Binary versions of evolutionary algorithms have emerged as alternatives to the state of the art methods for optimization in binary search spaces due to their simplicity and inexpensive computational cost. The adaption of such a binary version from an evolutionary algorithm is based on a transfer function that maps a continuous search space to a discrete search space. In an effort to identify the most efficient combination of transfer functions and algorithms, we investigate binary versions of Gravitational Search, Bat Algorithm, and Dragonfly Algorithm along with two families of transfer functions in unimodal and multimodal single objective optimization problems. The results indicate that the incorporation of the v-shaped family of transfer functions in the Binary Bat Algorithm significantly outperforms previous methods in this domain.

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. Larrañaga, P., Lozano, J.A.: Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation, vol. 2. Springer, New York (2001)

    MATH  Google Scholar 

  2. Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer (2011)

    Google Scholar 

  3. Tsamardinos, I., Brown, L.E., Aliferis, C.F.: The max-min hill-climbing Bayesian network structure learning algorithm. Mach. Learn. 65(1), 31–78 (2006)

    Article  Google Scholar 

  4. Ritthof, O., Klinkenberg, R., Fischer, S., Mierswa, I.: A hybrid approach to feature selection and generation using an evolutionary algorithm. In: UK Workshop on Computational Intelligence, pp. 147–154 (2002)

    Google Scholar 

  5. Raymer, M.L., Punch, W.F., Goodman, E.D., Kuhn, L.A., Jain, A.K.: Dimensionality reduction using genetic algorithms. IEEE Trans. Evol. Comput. 4(2), 164–171 (2000)

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: BGSA: binary gravitational search algorithm. Nat. Comput. 9(3), 727–745 (2010)

    Article  MathSciNet  Google Scholar 

  8. Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer (2010)

    Chapter  Google Scholar 

  9. Mirjalili, S., Mirjalili, S.M., Yang, X.S.: Binary bat algorithm. Neural Comput. Appl. 25(3–4), 663–681 (2014)

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Mirjalili, S., Lewis, A.: S-shaped versus v-shaped transfer functions for binary particle swarm optimization. Swarm Evol. Comput. 9, 1–14 (2013). http://www.sciencedirect.com/science/article/pii/S2210650212000648

    Article  Google Scholar 

  12. Suganthan, P., Hansen, N., Liang, J., Deb, K., Chen, Y.p., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization, pp. 341–357, January 2005

    Google Scholar 

  13. Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006)

    Article  Google Scholar 

  14. Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 8(3), 204–210 (2004)

    Article  Google Scholar 

  15. Liang, J.J., Suganthan, P.N.: Dynamic multi-swarm particle swarm optimizer with local search. In: The 2005 IEEE Congress on Evolutionary Computation, vol. 1, pp. 522–528. IEEE (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Netaji Subhas Institute of Technology, New Delhi, India

    Ramit Sawhney, Ravi Shankar & Roopal Jain

Authors
  1. Ramit Sawhney
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ravi Shankar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roopal Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ramit Sawhney .

Editor information

Editors and Affiliations

  1. BISITE Digital Innovation Hub. Edificio Multimedia I+D+i, University of Salamanca, Salamanca, Spain

    Fernando De La Prieta

  2. Faculty of Engineering, Department of Electronics, Information and Communication, Osaka Institute of Technology, Osaka, Osaka, Japan

    Sigeru Omatu

  3. Department of Computing Systems, School of Industrial Engineers at Albacete, University of Castilla-La Mancha, Albacete, Spain

    Antonio Fernández-Caballero

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sawhney, R., Shankar, R., Jain, R. (2019). A Comparative Study of Transfer Functions in Binary Evolutionary Algorithms for Single Objective Optimization. In: De La Prieta, F., Omatu, S., Fernández-Caballero, A. (eds) Distributed Computing and Artificial Intelligence, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-319-94649-8_4

Download citation

Publish with us

Policies and ethics

Search

Navigation