Skip to main content
SpringerLink
Log in

Improved Firefly Algorithm Based on Community and Migration Strategy and Its Application of PID Controller Design

  • Conference paper
  • First Online:
Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021) (AICV 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1377))

Abstract

The PID controller based on the Ziegler-Nichols Tuning Rules to obtain the parameters cannot achieve the best control performance. This paper proposes an optimization method of the PID controller parameters based on the improved firefly algorithm with community and migration strategies. The traditional firefly algorithm will cause the calculation to stagnate because the far distance between individuals induces the attraction term to approach zero. Therefore, community and migration strategies are introduced to solve the problem . And the comparative analysis is conducted through several typical evaluation functions. The simulation results show that the improved firefly algorithm can effectively avoid the algorithm stagnation and reduce the evaluation function’s evaluation times. Finally, it is applied to the PID controller’s parameter optimization to achieve a better control effect.

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. Astrom, K.J., Hagglund, T.: Automatic tuning of PID controllers. Instrument Society of America, Pennsylvania (1988)

    MATH  Google Scholar 

  2. Astrom, K.J., Hagglund, T.: Revisiting the Ziegler-Nichols step response method for PID control. J. Process Control 14(6), 635–650 (2004)

    Article  Google Scholar 

  3. Bahavarnia, M.S., Tavazoei, M.S.: A new view to Ziegler-Nichols step response tuning method: Analytic non-fragility justification. J. Process Control 23(1), 23–33 (2013)

    Article  Google Scholar 

  4. Wang, Y.J.: Determination of all feasible robust PID controllers for open-loop unstable plus time delay processes with gain margin and phase margin specifications. ISA Trans. 53(2), 628–646 (2014)

    Article  Google Scholar 

  5. Jin, Q.B., Liu, Q., Huang, B.: New results on the robust stability of PID controllers with gain and phase margins for UFOPTD processes. ISA Trans. 61, 240–250 (2016)

    Article  Google Scholar 

  6. Tzafestas, S., Papanikolopoulos, N.P.: Incremental fuzzy expert PID control. IEEE Trans. Ind. Electron. 37(5), 365–371 (1990)

    Article  Google Scholar 

  7. Chee, F., Fernando, T.L., Savkin, A.V., Heeden, V.V.: Expert PID control system for blood glucose control in critically ill patients. IEEE Trans. Inf. Technol. Biomed. 7(4), 419–425 (2003)

    Article  Google Scholar 

  8. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)

    Google Scholar 

  9. Yin, Z., Du, C., Liu, J., et al.: Research on autodisturbance-rejection control of induction motors based on an ant colony optimization algorithm. IEEE Trans. Ind. Electron. 65(4), 3077–3094 (2018)

    Article  Google Scholar 

  10. Yang, X.S.: Nature-inspired metaheuristic algorithm. Luniver Press, Beckington (2008)

    Google Scholar 

  11. Marichelvam, M.K., Prabaharan, T., Yang, X.S.: A discrete firefly algorithm for the multi-objective hybrid flowshop scheduling problem. IEEE Trans. Evol. Comput. 18(2), 301–305 (2014)

    Article  Google Scholar 

  12. Yang, X.S., Hosseini, S.S.S., Gandomi, A.H.: Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl. Soft Comput. 12(3), 1180–1186 (2012)

    Article  Google Scholar 

  13. Senthilnath, J., Omkar, S., Mani, V.: Clustering using firefly algorithm: performance study. Swarm Evol. Comput. 1(3), 164–171 (2011)

    Article  Google Scholar 

  14. Falcon, R., Almeida, M., Nayak, A.: Fault identification with binary adaptive fireflies in parallel and distributed systems. In: 2011 IEEE Congress on Evolutionary Computation (CEC), pp. 1359–1366, New Orleans, USA (2011)

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Acknowledgment

The Minjiang University partially supported this work under Grant MJY192026, 103952020001, 2019MHX100, MJIS2020D003, 2019L3009 JAT200444.

Author information

Authors and Affiliations

  1. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, 350121, China

    X. Q. Zhang, C. Y. Wu, L. Shi, J. R. Guo & L. Y. Jang

  2. Digital Fujian IoT Laboratory of Intelligent Production, Fuzhou, China

    X. Q. Zhang

  3. Industrial Design and Research Institute, Minjiang University, Fuzhou, China

    C. Y. Wu

Authors
  1. X. Q. Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Y. Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. L. Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J. R. Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. L. Y. Jang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Information Technology Department, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco

    Abdelkrim Haqiq

  3. School of Medicine, University of Missouri, Columbia, MO, USA

    Peter J. Tonellato

  4. ISAE-ENSMA, Futuroscope Chasseneuil Cedex, France

    Ladjel Bellatreche

  5. British University Vietnam, Hung Yen, Vietnam

    Sam Goundar

  6. Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

    Ahmad Taher Azar

  7. NEST Research Group, ENSEM, Hassan II University of Casablanca, Casablanca, Morocco

    Essaid Sabir

  8. High National School for Computer Science and Systems Analysis (ENSIAS), Mohammed V University, Rabat, Morocco

    Driss Bouzidi

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

Zhang, X.Q., Wu, C.Y., Shi, L., Guo, J.R., Jang, L.Y. (2021). Improved Firefly Algorithm Based on Community and Migration Strategy and Its Application of PID Controller Design. In: Hassanien, A.E., et al. Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021). AICV 2021. Advances in Intelligent Systems and Computing, vol 1377. Springer, Cham. https://doi.org/10.1007/978-3-030-76346-6_25

Download citation

Publish with us

Policies and ethics

Search

Navigation