Abstract
This work presents a crowding-distance(CD)-based multiobjective artificial bee colony algorithm for Proportional-Integral-Derivative (PID) parameter optimization. In the proposed algorithm, a new fitness assignment method is defined based on the nondominated rank and the CD. An archive set is introduced for saving the Pareto optimal solutions, and the CD is also used to wipe off the extra solutions in the archive. The experimental results compared with NSGAII over two test functions show its effectiveness, and the simulation results of PID parameter optimization verify that it is efficient for applications.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Khodabakhshian, A., Hooshmand, R.: A new PID controller design for automatic generation control of hydro power systems. Int. J. Electr. Power Energy Syst. 32, 375–382 (2010)
Rani, M.R., Selamat, H., Zamzuri, H., et al.: Multiobjective optimization for PID controller tuning using the global ranking genetic algorithm. International Journal of Innovative Computing, Information and Control 8, 269–284 (2012)
Deb, K., Pratap, A., Agarwal, S., et al.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 2, 182–197 (2002)
Karaboga, D.: An Idea Based on Honey bee Swarm for Numerical Optimization. Technical Report, Computer Engineering Department, Erciyes University, Turkey (2005)
Luo, B., Zheng, J., Xie, J., et al.: Dynamic Crowding Distance–A New Diversity Maintenance Strategy for MOEAs. In: The Proceedings of the Fourth International Conference on Natural Computation, pp. 580–585. IEEE (2008)
Zhou, A., Qu, B.Y., Li, H., et al.: Multiobjective Evolutionary Algorithms: A Survey of the State-of-the-art. Journal of Swarm and Evolutionary Computation 1, 32–49 (2011)
Zhao, L., Ju, G., Lu, J.: An Improved Genetic Algorithm in Multi-objective Optimization and its Application. Proceedings of the CSEE 28(2), 96–102 (2008)
Li, M., Shen, J.: Simulating study of adaptive GA-based PID parameter optimization for the control of superheated steam temperature. Proceedings of the CSEE 22(8), 145–149 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhou, X., Shen, J., Li, Y. (2014). Crowding-Distance-Based Multiobjective Artificial Bee Colony Algorithm for PID Parameter Optimization. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8794. Springer, Cham. https://doi.org/10.1007/978-3-319-11857-4_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-11857-4_25
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11856-7
Online ISBN: 978-3-319-11857-4
eBook Packages: Computer ScienceComputer Science (R0)