Skip to main content
Springer Nature Link
Log in

A Competitive Social Spider Optimization with Learning Strategy for PID Controller Optimization

  • Conference paper
  • First Online:
Simulated Evolution and Learning (SEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10593))

Included in the following conference series:

Abstract

Tuning the parameters of PID controller is a difficult problem, since it is hard to get the optimum parameters by the traditional methods, new methods are required. Nature-inspired algorithms perform powerfully and efficiently on global optimization problems. Social spider optimization (SSO) is one of the novel nature-inspired algorithms, and it exhibits good performance on avoiding premature convergence. However, the efficiency of SSO degrades when used in applications such as PID controller optimization whose objective function with highly correlated variables. In order to overcome this disadvantage, based on the SSO, a competitive social spider optimization (CSSO) is proposed in this paper. To enhance the performance of SSO, we regroup the spiders and the diversity of population is increased. Inspired by the competitive mating behavior of spiders, the competitive mating mechanism is introduced, and a learning strategy is used for the new born spider. The CSSO is applied to optimize the parameters of PID controller, and the simulation results show that the performance of CSSO is promising in PID controller optimization.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Yongzhong, L., Yan, D., Zhang, J., Levy, D.: A variant with a time varying pid controller of particle swarm optimizers. Inf. Sci. 297, 21–49 (2015)

    Article  Google Scholar 

  2. Wei, C., Söffker, D.: Optimization strategy for PID-controller design of AMB rotor systems. IEEE Trans. Control Syst. Technol. 24(3), 788–803 (2016)

    Article  Google Scholar 

  3. Visioli, A.: Tuning of PID controllers with fuzzy logic. IEE Proc. Control Theory Appl. 148(1), 1–8 (2001)

    Article  MathSciNet  Google Scholar 

  4. Gaing, Z.L.: A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Trans. Energy Convers. 19(2), 384–391 (2004)

    Article  Google Scholar 

  5. Feng, X., Zou, R., Yu, H.: A novel optimization algorithm inspired by the creative thinking process. Soft. Comput. 19(10), 2955–2972 (2015)

    Article  Google Scholar 

  6. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (2002)

    Google Scholar 

  7. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report, Technical report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)

    Google Scholar 

  8. Cuevas, E., Cienfuegos, M., Zaldvar, D., Rez-Cisneros, M.: A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Syst. Appl. Int. J. 40(16), 6374–6384 (2016)

    Article  Google Scholar 

  9. Shareef, H., Ibrahim, A.A., Mutlag, A.H.: Lightning search algorithm. Appl. Soft Comput. 36, 315–333 (2015)

    Article  Google Scholar 

  10. Clutton-Brock, T.: Sexual selection in males and females. Science 318(5858), 1882–1885 (2007)

    Article  Google Scholar 

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

    MATH  Google Scholar 

  12. Aviles, L.: Sex-ratio bias and possible group selection in the social spider anelosimus eximius. Am. Nat. 128(1), 1–12 (1986)

    Article  Google Scholar 

  13. Keiser, C.N., Jones, D.K., Modlmeier, A.P., Pruitt, J.N.: Exploring the effects of individual traits and within-colony variation on task differentiation and collective behavior in a desert social spider. Behav. Ecol. Sociobiol. 68(5), 839–850 (2014)

    Article  Google Scholar 

  14. Modlmeier, A.P., Laskowski, K.L., Brittingham, H.A., Coleman, A., Knutson, K.A., Kuo, C., McGuirk, M., Zhao, K., Keiser, C.N., Pruitt, J.N.: Adult presence augments juvenile collective foraging in social spiders. Anim. Behav. 109, 9–14 (2015)

    Article  Google Scholar 

  15. Modlmeier, A.P., Keiser, C.N., Watters, J.V., Sih, A., Pruitt, J.N.: The keystone individual concept: an ecological and evolutionary overview. Anim. Behav. 89, 53–62 (2014)

    Article  Google Scholar 

  16. Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1(1), 3–18 (2011)

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61472139 and 61462073, the Information Development Special Funds of Shanghai Economic and Information Commission under Grant No. 201602008, the Open Funds of Shanghai Smart City Collaborative Innovation Center.

Author information

Authors and Affiliations

  1. Department of Computer Science, The East University of Science and Technology, Shanghai, China

    Zhaolin Lai, Xiang Feng & Huiqun Yu

  2. Smart City Collaborative Innovation Center, The Shanghai Jiao Tong University, Shanghai, China

    Xiang Feng

Authors
  1. Zhaolin Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiang Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huiqun Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiang Feng .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  2. City University of Hong Kong, Hong Kong, Kowloon, Hong Kong

    Kay Chen Tan

  3. Victoria University of Wellington, Wellington, Wellington, New Zealand

    Mengjie Zhang

  4. Southern University of Science and Technology, Shenzhen, China

    Ke Tang

  5. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

  6. City University of Hong Kong, Kowloon Tong, Hong Kong

    Qingfu Zhang

  7. Peking University, Beijing, China

    Ying Tan

  8. University of Leipzig, Leipzig, Germany

    Martin Middendorf

  9. University of Surrey, Guildford, Surrey, United Kingdom

    Yaochu Jin

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Lai, Z., Feng, X., Yu, H. (2017). A Competitive Social Spider Optimization with Learning Strategy for PID Controller Optimization. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_85

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68759-9_85

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68758-2

  • Online ISBN: 978-3-319-68759-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics