Skip to main content
SpringerLink
Log in

Performance Analysis of Whale Optimization Algorithm

  • Conference paper
  • First Online:
Communications, Signal Processing, and Systems (CSPS 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 516))

Abstract

Through the research and analysis of a relatively novel natural heuristic, meta-heuristic swarm intelligence optimization algorithm, this swarm intelligence algorithm is defined as a whale optimization algorithm. The algorithm builds a mathematical model by simulating a social behavior of humpback whales. This optimization algorithm was inspired by the bubble-like net hunting phenomenon that humpback whales prey on. By analyzing the four benchmark optimization problems with or without offset and rotation, the convergence performance of the whale optimization algorithm and the ability to solve the optimization problem are proved. The performance of the whale optimization algorithm is based on the computer simulation technology. Through the convergence curve obtained from the experiment, we can see that the whale optimization algorithm performs best for the five benchmark optimization problems without rotation.

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
Hardcover Book
USD 219.99
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. Gotmare A, Bhattacharjee SS, Patidar R. Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review. Swarm Evol Comput. 2017;32:68–84.

    Article  Google Scholar 

  2. Yuen SY, Chow CK. A genetic algorithm that adaptively mutates and never revisits. IEEE Trans Evol Comput. 2009;13:454–72.

    Article  Google Scholar 

  3. Mirjalili S, Lewis A. The whale optimization algorithm. Adv Eng Softw. 2016;95:51–67.

    Article  Google Scholar 

  4. Zhang, X, Zhang, X, Fu, WN. Fast numerical method for computing resonant characteristics of electromagnetic devices based on finite-element method. IEEE Trans Magn. 2017;53, Article No. 7401004.

    Google Scholar 

  5. Wu Z, Xia X. Optimal switching renewable energy system for demand side management. Solar Energy. 2015;114:278–88.

    Article  Google Scholar 

  6. Zhang X, Zhang X. Improving differential evolution by differential vector archive and hybrid repair method for global optimization. Soft Comput. 2017;21:7107–16.

    Article  Google Scholar 

  7. Oliva D, El Aziz MA, Hassanien AE. Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm. Appl Energy. 2017;200:141–54.

    Article  Google Scholar 

  8. Zhang S, Zhou Y, Li Z, Pan W. Grey wolf optimizer for unmanned combat aerial vehicle path planning. Adv Eng Softw. 2016;99:121–36.

    Article  Google Scholar 

  9. Cheng MY, Prayogo D. Fuzzy adaptive teaching clearing-based optimization for global numerical optimization. Neural Comput Appl. 2018;29:309–27.

    Article  Google Scholar 

  10. Gaudioso M, Giallombardo G, Mukhametzhanov M. Numerical infinitesimals in a variable metric method for convex nonsmooth optimization. Appl Math Comput. 2018;318:312–20.

    MATH  Google Scholar 

  11. Li G, Cui L, Fu X, Wen Z, Lu N, Lu J. Artificial bee colony algorithm with gene recombination for numerical function optimization. Appl Soft Comput. 2017;52:146–59.

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported in part by the National Natural Science Foundation of China (Project No. 61601329, 61603275), the Tianjin Higher Education Creative Team Funds Program, the Applied Basic Research Program of Tianjin (Project No. 15JCYBJC51500, 15JCYBJC52300), and the Doctoral Fund Project of Tianjin Normal University (Project No. 043-135202XB1602).

Author information

Authors and Affiliations

  1. Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin, China

    Xin Zhang, Dongxue Wang & Xiu Zhang

  2. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Xin Zhang, Dongxue Wang & Xiu Zhang

Authors
  1. Xin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dongxue Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiu Zhang .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Xin Liu

  3. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, X., Wang, D., Zhang, X. (2020). Performance Analysis of Whale Optimization Algorithm. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_47

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-6504-1_47

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6503-4

  • Online ISBN: 978-981-13-6504-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation