Skip to main content
SpringerLink
Log in

Modified Flower Pollination Algorithm for Function Optimization

  • Conference paper
  • First Online:
Intelligent Computing and Optimization (ICO 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1324))

Included in the following conference series:

Abstract

The flower pollination algorithm (FPA) was firstly proposed in 2012 as one of the population-based metaheuristic optimization search techniques. It is conceptualized by the pollination behavior of flowering plants. In this paper, the new enhanced version of the original FPA named the modified flower pollination algorithm (MoFPA) is proposed to improve its search performance for function optimization. The switching probability of the original FPA used for selection between local and global pollinations is changed from the fixed manner to the random manner according to the pollination behavior of flowering plants in nature. To perform its effectiveness, the proposed MoFPA is tested against ten standard benchmark functions compared with the original FPA. As simulation results, it was found that the proposed MoFPA performs superior search performance for function optimization to the original FPA with higher success rates and faster search time consumed.

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 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

Institutional subscriptions

References

  1. Glover, F., Kochenberger, G.A.: Handbook of Metaheuristics. Kluwer Academic Publishers, Dordrecht (2003)

    Book  Google Scholar 

  2. Talbi, E.G.: Metaheuristics Forn Design to Implementation. Wiley, Hoboken (2009)

    MATH  Google Scholar 

  3. Yang, X.S.: Flower pollination algorithm for global optimization. In: Unconventional Computation and Natural Computation. LNCS, vol. 7445, pp. 240–249 (2012)

    Google Scholar 

  4. Yang, X.S., Karamanoglu, M., He, X.S.: Flower pollination algorithm: a novel approach for multiobjective optimization. Eng. Optim. 46(9), 1222–1237 (2014)

    Article  MathSciNet  Google Scholar 

  5. He, X., Yang, X.S., Karamanoglu, M., Zhao, Y.: Global convergence analysis of the flower pollination algorithm: a discrete-time Markov chain approach. In: International Conference on Computational Science (ICCS 2017), pp. 1354–1363 (2017)

    Google Scholar 

  6. Chiroma, H., Shuib, N.L.M., Muaz, S.A., Abubakar, A.I., Ila, L.B., Maitama, J.Z.: A review of the applications of bio-inspired flower pollination algorithm. Procedia Comput. Sci. 62, 435–441 (2015)

    Article  Google Scholar 

  7. Nabil, E.: A modified flower pollination algorithm for global optimization. Expert Syst. Appl. 57, 192–203 (2016)

    Article  Google Scholar 

  8. Rodrigues, D., Yang, X.S., De Souza, A.N., Papa, J.P.: Binary flower pollination algorithm and its application to feature selection. In: Recent Advances in Swarm Intelligence and Evolutionary Computation, pp. 85–100. Springer, Cham (2015)

    Google Scholar 

  9. Shambour, M.Y., Abusnaina, A.A., Alsalibi, A.I.: Modified global flower pollination algorithm and its application for optimization problems. Interdisc. Sci. Comput. Life Sci. 11, 1–12 (2018)

    Google Scholar 

  10. Willmer, P.: Pollination and Floral Ecology. Princeton University Press, Princeton (2011)

    Book  Google Scholar 

  11. Balasubramani, K., Marcus, K.: A study on flower pollination algorithm and its applications. Int. J. Appl. Innov. Eng. Manag. 3, 320–325 (2014)

    Google Scholar 

  12. Pavlyukevich, I.: Lévy flights, non-local search and simulated annealing. J. Comput. Phys. 226, 1830–1844 (2007)

    Article  MathSciNet  Google Scholar 

  13. 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, 635–672 (2005)

    Article  MathSciNet  Google Scholar 

  14. Jamil, M., Yang, X.S.: A literature survey of benchmark functions for global optimization problems. Int. J. Math. Model. Numer. Optim. 4(2), 150–194 (2013)

    MATH  Google Scholar 

  15. Kashif, H., Mohd, N.M.S., Shi, C., Rashid, N.: Common benchmark functions for metaheuristic evaluation: a review. Int. J. Inf. Visual. 1(4–2), 218–223 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Southeast Asia University, 19/1 Petchkasem Road, Nongkhangphlu, Nonghkaem, Bangkok, 10160, Thailand

    Noppadol Pringsakul & Deacha Puangdownreong

Authors
  1. Noppadol Pringsakul
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deacha Puangdownreong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Deacha Puangdownreong .

Editor information

Editors and Affiliations

  1. Department of Fundamental and Applied Sciences, Universiti Teknologi Petronas, Tronoh, Perak, Malaysia

    Pandian Vasant

  2. Faculty of Electrical Engineering and Computer Science, VÅ B TU Ostrava, Ostrava, Czech Republic

    Ivan Zelinka

  3. Faculty of Engineering Management, Poznan University of Technology, Poznan, Poland

    Gerhard-Wilhelm Weber

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Pringsakul, N., Puangdownreong, D. (2021). Modified Flower Pollination Algorithm for Function Optimization. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_18

Download citation

Publish with us

Policies and ethics

Search

Navigation