Abstract
Fruit fly optimization algorithm (FOA) is a novel bio-inspired technique, which has attracted a lot of researchers’ attention. In order to improve the performance of FOA, a modified FOA is proposed which adopts the phase angle vector to encoded the fruit fly location and brings in the double sub-swarms mechanism. This new strategies can enhance the search ability of the fruit fly and helps find the better solution. Simulation experiments have been conducted on fifteen benchmark functions and the comparisons with the basic FOA show that θ-DFOA performs better in terms of solution accuracy and convergence speed. In addition, the proposed algorithm is used to optimization the PID controller, and the promising performance is achieved.
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
Similar content being viewed by others
References
Pan, W.T.: A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl.-Based Syst. 26, 69–74 (2012)
Zheng, X.L., Wang, L., Wang, S.Y.: A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem. Knowl.-Based Syst. 57, 95–103 (2014)
Lin, S.M.: Analysis of service satisfaction in web auction logistics service using a combination of fruit fly optimization algorithm and general regression neural network. Neural Comput. Appl. 22(3–4), 783–791 (2013)
Li, H.Z., Guo, S., Li, C.J., Sun, J.Q.: A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowl.-Based Syst. 37, 378–387 (2013)
Sheng, W., Bao, Y.: Fruit fly optimization algorithm based fractional order fuzzy-PID controller for electronic throttle. Nonlinear Dyn. 73(1–2), 611–619 (2013)
Arya, Y., Kumar, N.: BFOA-scaled fractional order fuzzy PID controller applied to AGC of multi-area multi-source electric power generating systems. Swarm Evol. Comput. 32, 202–218 (2017)
Zhang, X.Y., Jia, S.M., Li, X.Z., Jian, M.: Design of the fruit fly optimization algorithm based path planner for UAV in 3D environments. In: Proceedings of 2017 IEEE International Conference on Mechatronics and Automation, pp. 381–386. IEEE, Takamatsu (2017)
Meng, T., Pan, Q.K.: An improved fruit fly optimization algorithm for solving the multidimensional knapsack problem. Appl. Soft Comput. 50, 79–93 (2017)
Yuan, X.F., Liu, Y.M., Xiang, Y.Z., Yan, X.G.: Parameter identification of BIPT system using chaotic-enhanced fruit fly optimization algorithm. Appl. Math. Comput. 268, 1267–1281 (2015)
Kanarachos, S., Griffin, J., Fitzpatrick, M.E.: Efficient truss optimization using the contrast-based fruit fly optimization algorithm. Comput. Struct. 182, 137–148 (2017)
Pan, Q.K., Sang, H.Y., Duan, J.H., Gao, L.: An improved fruit fly optimization algorithm for continuous function optimization problems. Knowl.-Based Syst. 62, 69–83 (2014)
Mitić, M., Vuković, N., Petrović, M., Miljković, Z.: Chaotic fruit fly optimization algorithm. Knowl.-Based Syst. 89, 446–458 (2015)
Yuan, X.F., Dai, X.S., Zhao, J.Y., He, Q.: On a novel multi-swarm fruit fly optimization algorithm and its application. Appl. Math. Comput. 233, 260–271 (2014)
Acknowledgement
This work is supported by National Natural Science Foundation of China (No. 61703012 and 61563011), Beijing Natural Science Foundation (No. 4182010), and the BJUT Promotion Project on Intelligent Manufacturing (No. 040000546317552).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Zhang, X., Chen, G., Jia, S. (2018). Parameters Optimization of PID Controller Based on Improved Fruit Fly Optimization Algorithm. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-93815-8_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93814-1
Online ISBN: 978-3-319-93815-8
eBook Packages: Computer ScienceComputer Science (R0)