CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A Comparison of DE and SFLA Optimization Algorithms in Tuning Parameters of Fuzzy Logic Controller

by Duc Hoang Nguyen
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 156 - Number 11
Year of Publication: 2016
Authors: Duc Hoang Nguyen
10.5120/ijca2016912557

Duc Hoang Nguyen . A Comparison of DE and SFLA Optimization Algorithms in Tuning Parameters of Fuzzy Logic Controller. International Journal of Computer Applications. 156, 11 ( Dec 2016), 17-22. DOI=10.5120/ijca2016912557

@article{ 10.5120/ijca2016912557,
author = { Duc Hoang Nguyen },
title = { A Comparison of DE and SFLA Optimization Algorithms in Tuning Parameters of Fuzzy Logic Controller },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 156 },
number = { 11 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 17-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume156/number11/26753-2016912557/ },
doi = { 10.5120/ijca2016912557 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:02:20.534717+05:30
%A Duc Hoang Nguyen
%T A Comparison of DE and SFLA Optimization Algorithms in Tuning Parameters of Fuzzy Logic Controller
%J International Journal of Computer Applications
%@ 0975-8887
%V 156
%N 11
%P 17-22
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The paper presents using Differential Evolution (DE) and Shuffled Frog Leaping Algorithm (SFLA) to optimally tune parameters of a fuzzy logic controller stabilizing a rotary inverted pendulum system at its upright equilibrium position. Both the DE and SFLA are meta-heuristic search methods. DE belongs to the class of evolutionary algorithms while SFLA is inspired from the memetic evolution of a group of frogs when seeking for food. In this study, the rule base of the Fuzzy Logic Controller (FLC) is brought by expert experience, and the parameters of the controller, i.e. the membership function parameters and scaling gains, are optimally tuned by the DE and SFLA such that a predefined criterion is minimized. Simulation results show that the designed fuzzy controller is able to balance the rotary inverted pendulum system around its equilibrium state. Besides, convergent rate of SFLA is faster than that of DE but DE has ability to find optimal solutions better than SFLA does.

References
  1. K.M. Passino, and S. Yurkovich. “Fuzzy logic”, Menlo Park, CA: Addison-Wesley Longman, (1998).
  2. F. Herrera, M. Lozano, and J.L. Verdegay. “Tuning fuzzy logic controllers by genetic algorithms”, International Journal of Approximate Reasoning, 12: 299- 315, (1995)
  3. Feng M, “Particle swarm optimisation learning fuzzy systems design”, Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02.
  4. D.T. Pham, A.Haj Dqrwish, E.E. Eldukhri, and S. Otri. “Using the Bees Algorithm to tune a fuzzy logic controller for a robot gymnast”, Proceedings of the 3rd Virtual International Conference on Intelligent Production Machines and Systems, (2007).
  5. C.-F. Juang, H.-J. Huang; C.-M. Lu. “Fuzzy Controller Design by Ant Colony Optimization”, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2007), pp.1-5, (2007)
  6. Duc-Hoang Nguyen and Thai-Hoang Huynh. “A SFLA-Based Fuzzy Controller for Balancing a Ball and Beam System”, Tenth IEEE International Conference on Control, Automation, Robotics and Vision (ICARCV 2008), Hanoi, Vietnam,  (17-20, 2008).
  7. Niranjan Nayak, Sangram Keshari Routray, Pravat Kumar Rout, “Design of Takagi-Sugeno fuzzy controller for VSC-HVDC parallel AC transmission system using differential evolution algorithm”, International Journal of Computer Aided Engineering and Technology 2016, Volume 8, Issue 3.
  8. Quanser.: SRV02-Series Rotary Experiment # 7, “Rotary Inverted Pendulum”.
  9. R. Storn and K. Price, “Differential Evolution - A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces,” Tech. Report, International Computer Science Institute (Berkeley), 1995.
  10. R. Storn and K. Price, “Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces”, Journal of Global Optimization, vol. 11, Dec. 1997, pp. 341-359.
  11. Neri, F. & Tirronen, “Recent advances in differential evolution: A survey and experimental analysis”, Artificial Intelligence Review 33(1-2): 61-106. V. 2010.
  12. Swagatam Das, Sankha Subhra Mullick, and P. N. Suganthan, “Recent Advances in Differential Evolution – An Updated Survey”, Swarm and Evolutionary Computation, Volume 27, April 2016, Pages 1–30.
  13. K. V. Price, R. M. Storn, and J. A. Lampinen, “Differential Evolution: A Practical Approach to Global Optimization”, Springer-Verlag, Berlin, Heidelberg, second edition, 2006
  14. M.-F. Han et al,“Differential evolution with local information for neuro-fuzzy systems optimization”, Knowledge-Based Systems 44 (2013) 78–89, Elsevier 2013.
  15. Cheng-Jian Lin, Chih-Feng Wu, Hsueh-Yi Lin & Cheng-Yi Yu, “An Interactively Recurrent Functional Neural Fuzzy Network with Fuzzy Differential Evolution and Its Applications”, Sains Malaysiana 44(12)(2015): 1721–1728.
  16. Patricia Ochoa, Oscar Castillo and José Soria, "Differential Evolution with Dynamic Adaptation of Parameters for the Optimization of Fuzzy Controllers", Recent Advances on Hybrid Approaches for Designing Intelligent Systems Studies in Computational Intelligence 547, Springer 2014.
  17. Muzaffar Eusuff, Kevin Lansey and Fayzul Pasha, “Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization”, Engineering Optimization Vol. 38, No. 2, March 2006, 129–154.
  18. A. Darvishi, A. Alimardani, B. Vahidi , S.H. Hosseinian, “Shuffled Frog-Leaping Algorithm for Control of Selective and Total Harmonic Distortion”, Journal of Applied Research and Technology, Volume 12, Issue 1, February 2014, Pages 111–121.
  19. Kaushik Kumar Bhattacharjee , S.P. Sarmah, “Shuffled frog leaping algorithm and its application to 0/1 knapsack problem”, Applied Soft Computing, Volume 19, June 2014, Pages 252–263.
  20. Yi Han, et al,“Shuffled Frog Leaping Algorithm for Preemptive Project Scheduling Problems with Resource Vacations Based on Patterson Set”, Journal of Applied Mathematics,Volume 2013 (2013), Article ID 451090, 15 pages.
  21. Daniel Mora-Melia, Pedro L. Iglesias-Rey, F. Javier Martínez-Solano and Pedro Muñoz-Velasco, “The Efficiency of Setting Parameters in a Modified Shuffled Frog Leaping Algorithm Applied to Optimizing Water Distribution Networks”, Water 2016, 8, 182, MDPI.
  22. Dina M. Said, Nabil M. Hamed, Almoataz Y. Abdelaziz, “Shuffled Frog Leaping Algorithm for Economic Dispatch with Valve Loading Effect”, International Electrical Engineering Journal, Vol 7 No 3, 30 JUL, 2016.
Index Terms

Computer Science
Information Sciences

Keywords

Optimization DE SFLA Fuzzy Controller

Powered by PhDFocusTM