Skip to main content
SpringerLink
Log in

IFPDSO-PS: A Hybrid Approach for Global and Local Optimization

  • Conference paper
  • First Online:
Recent Advances in Soft Computing and Data Mining (SCDM 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 457))

Included in the following conference series:

  • 237 Accesses

Abstract

The nature inspired algorithms have motivated the practitioners to solve complex real-world problems. These algorithms are more capable to approach the optimal solution faster than conventional methods. The proposed algorithm uses the exploration capability of the Improved Flower Pollination algorithm with dynamic switch probability and swap operator (IFPDSO) and exploitation capability of Pattern Search (PS) to approach the optimal solution efficiently. The hybridization of IFPDSO and Pattern Search (IFPDSO-PS) has been validated on various benchmark functions and compared with other hybrid algorithms to evaluate its better performance.

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 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
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. Hussain, K., Mohd Salleh, M.N., Cheng, S., Shi, Y.: Metaheuristic research: a comprehensive survey. Artif. Intell. Rev. 1–43 (2018)

    Google Scholar 

  2. Ratniyomchai, T., et al.: Preface. Stud. Comput. Intell. 7(1), v–vi (2016)

    Google Scholar 

  3. Chong, C.S., Sivakumar, Low, A.I.,M.Y.H., Gay, K.L.: A bee colony optimization algorithm to job shop scheduling. In: Proceedings of the Winter Simulation Conference, no. December, pp. 1954–1961 (2006)

    Google Scholar 

  4. Firpi, H.A., Vogelstein, R.J.: Particle swarm optimization-based feature selection for cognitive state detection. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), pp. 6556–6559 (2011)

    Google Scholar 

  5. Yang, X.S.: Preface. Stud. Comput. Intell. 585, v–vi (2014)

    MATH  Google Scholar 

  6. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. J. 8(1), 687–697 (2008)

    Article  Google Scholar 

  7. Yang, X.-S.: Firefly algorithm, stochastic test functions and design optimisation (2010). arXiv Prepr. arXiv1003.1409

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  9. Mirjalili, S.M., et al.: The whale optimization algorithm. Adv. Eng. Softw. 27(2), 46–61 (2016)

    Google Scholar 

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

    Article  Google Scholar 

  11. Wang, Z., Luo, Q., Zhou, Y.: Hybrid metaheuristic algorithm using butterfly and flower pollination base on mutualism mechanism for global optimization problems, no. 0123456789. Springer, London (2020)

    Google Scholar 

  12. Yang, X.-S., Yang, X.-S.: Chapter 6 – differential evolution. Nat. Inspir. Optim. Algorithm 89–97 (2014)

    Google Scholar 

  13. Heiss-Czedik, D.: An introduction to genetic algorithms. Artif. Life 3(1), 63–65 (1997)

    Article  Google Scholar 

  14. Ghate, A., Smith, R.L.: Adaptive search with stochastic acceptance probabilities for global optimization. Oper. Res. Lett. 36(3), 285–290 (2008)

    Article  MathSciNet  Google Scholar 

  15. Valdez, F., Melin, P., Castillo, O.: A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation. Expert Syst. Appl. 41(14), 6459–6466 (2014)

    Article  Google Scholar 

  16. Yang, X.-S.: Flower pollination algorithm for global optimization. In: Durand-Lose, J., Jonoska, N. (eds.) UCNC 2012. LNCS, vol. 7445, pp. 240–249. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32894-7_27

    Chapter  Google Scholar 

  17. Chakraborty, D., Saha, S., Maity, S.: Training feedforward neural networks using hybrid flower pollination-gravitational search algorithm. In: 2015 1st International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE) 2015, pp. 261–266 (2015)

    Google Scholar 

  18. Popescu, E., Popescu, N.A.: Models for heavy tailed data and applications. AIP Conf. Proc. 1043, 328–332 (2008)

    Article  Google Scholar 

  19. Iqbal, M., Nawi, N.M., Mohamad, R.B.: An improved flower pollination solution for economic dispatch with valve point effect. Indones. J. Electr. Eng. Comput. Sci. 22(2) 629 (2021)

    Google Scholar 

  20. Zhang, C., Liu, C., Zhang, X., Almpanidis, G.: An up-to-date comparison of state-of-the-art classification algorithms. Expert Syst. Appl. 82, 128–150 (2017)

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to express the deepest appreciation to support by Universiti Tun Hussein Onn Malaysia (UTHM) through Tier 1 vot.H938.

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, Johor, Malaysia

    Muhammad Iqbal Kamboh, Nazri Mohd Nawi & Radiah Mohamad

Authors
  1. Muhammad Iqbal Kamboh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nazri Mohd Nawi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Radiah Mohamad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad Iqbal Kamboh .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Rozaida Ghazali

  2. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Nazri Mohd Nawi

  3. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Mustafa Mat Deris

  4. School of Information Technology Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC, Australia

    Jemal H. Abawajy

  5. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Nureize Arbaiy

Rights and permissions

Reprints and permissions

Copyright information

© 2022 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

Kamboh, M.I., Nawi, N.M., Mohamad, R. (2022). IFPDSO-PS: A Hybrid Approach for Global and Local Optimization. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_23

Download citation

Publish with us

Policies and ethics

Search

Navigation