Skip to main content
SpringerLink
Log in

A Novel Swarm Intelligence Based Optimization Method: Harris’ Hawk Optimization

  • Conference paper
  • First Online:
Intelligent Systems Design and Applications (ISDA 2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 941))

Abstract

Swarm intelligence is a modern optimization technique, and one of the most promising techniques for solving optimization problems. In this paper, a new swarm intelligence based algorithm namely, Harris’ Hawk Optimizer (HHO) is proposed. The algorithm mimics the cooperative hunting behaviour of Harris’ hawks. The algorithm is analysed for twenty five well known benchmark functions. Performance of HHO is compared with Particle Swarm Optimization (PSO), Differential Evolution (DE), Grey Wolf Optimizer (GWO) and The Whale Optimization Algorithm (WOA). HHO is implemented and results present HHO as one of the efficient optimization methods.

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Rao, S.S.: Engineering Optimization: Theory and Practice. Wiley, Hoboken (2009)

    Book  Google Scholar 

  2. Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66–72 (1992)

    Article  Google Scholar 

  3. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  Google Scholar 

  4. Rechenberg, I.: Evolution strategy: nature’s way of optimization. In: Optimization: Methods and applications, possibilities and limitations, pp. 106–126. Springer, Heidelberg (1989)

    Google Scholar 

  5. Glover, F.: Tabu search—part I. ORSA J. Comput. 1(3), 190–206 (1989)

    Article  MathSciNet  Google Scholar 

  6. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995, MHS 1995, pp. 39–43. IEEE (1995)

    Google Scholar 

  7. 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 

  8. Karaboga, D.: An idea based on honey bee swarm for numerical optimization, Technical report-tr06, Erciyes university, engineering faculty, computer engineering department, vol. 200 (2005)

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  12. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Article  Google Scholar 

  13. Zoologger: the only raptor known to hunt in cooperative packs, New Scientist. https://www.newscientist.com

  14. Coulson, J.O., Coulson, T.D.: Group hunting by Harris’ hawks in Texas. J. Raptor Res. 29(4), 265–267 (1995)

    Google Scholar 

  15. Bednarz, J.C.: Cooperative hunting in Harris’ hawks (Parabuteo unicinctus). Science 239(4847), 1525 (1988)

    Article  Google Scholar 

  16. Olorunda, O., Engelbrecht, A.P.: Measuring exploration/exploitation in particle swarms using swarm diversity. In: IEEE Congress on Evolutionary Computation, 2008, CEC 2008, (IEEE World Congress on Computational Intelligence), pp. 1128–1134, IEEE (2008)

    Google Scholar 

  17. Alba, E., Dorronsoro, B.: The exploration/exploitation tradeoff in dynamic cellular genetic algorithms. IEEE Trans. Evol. Comput. 9(2), 126–142 (2005)

    Article  Google Scholar 

  18. Crepinsek, M., Liu, S.H., Mernik, M.: Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput. Surv. (CSUR) 45(3), 35 (2013)

    Article  Google Scholar 

  19. Ali, M.M., Khompatraporn, C., Zabinsky, Z.B.: A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. J. Global Optim. 31(4), 635–672 (2005)

    Article  MathSciNet  Google Scholar 

  20. Bansal, J.C., Sharma, H., Nagar, A., Arya, K.V.: Balanced artificial bee colony algorithm. Int. J. Artif. Intell. Soft Comput. 3(3), 222–243 (2013)

    Article  Google Scholar 

  21. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Malaviya National Institute of Technology Jaipur, Jaipur, India

    Divya Bairathi & Dinesh Gopalani

Authors
  1. Divya Bairathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dinesh Gopalani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Divya Bairathi .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bairathi, D., Gopalani, D. (2020). A Novel Swarm Intelligence Based Optimization Method: Harris’ Hawk Optimization. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_81

Download citation

Publish with us

Policies and ethics

Search

Navigation