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An Efficient Modified Shuffled Frog Leaping Optimization Algorithm

International Journal of Computer Applications
© 2011 by IJCA Journal
Number 1 - Article 1
Year of Publication: 2011
Mohammad Pourmahmood Aghababa
Mohammd Esmaeel Akbari
Amin Mohammadpour Shotorbani

Mohammad Pourmahmood Aghababa, Mohammd Esmaeel Akbari and Amin Mohammadpour Shotorbani. Article: An Efficient Modified Shuffled Frog Leaping Optimization Algorithm. International Journal of Computer Applications 32(1):26-30, October 2011. Full text available. BibTeX

	author = {Mohammad Pourmahmood Aghababa and Mohammd Esmaeel Akbari and Amin Mohammadpour Shotorbani},
	title = {Article: An Efficient Modified Shuffled Frog Leaping Optimization Algorithm},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {32},
	number = {1},
	pages = {26-30},
	month = {October},
	note = {Full text available}


In this paper, a modified shuffled frog leaping (MSFL) algorithm is proposed to overcome drawbacks of standard shuffled frog leaping (SFL) method. The MSFL approach is based on two major modifications on the conventional SFL method: (1) an adaptive accelerated position changing of frogs and (2) sweeping between randomly selected frogs (called superseding frogs). The first modification causes a fast convergence rate and consequently achieving a rapid adaptive algorithm, while the second one causes a better diversification and consequently escaping from local optimum traps. The MSFL algorithm performance is validated using benchmark functions. Simulation results indicate the superiority of MSFL to that of the original SFL in terms of optimal precision and fast convergence rate.


  • Z.L. Gaing, A Particle Swarm Optimization Approach for Optimum Design of PID controller in AVR system, IEEE Transactions on Energy Conversion, Vol 9(2), 2003, pp. 384-391.
  • Z.Y. Zhao, M. Tomizuka, and S. Isaka, Fuzzy gain scheduling of PID controllers, IEEE Trans. System, Man, and Cybernetics, Vol. 23, No. 5, 1993, pp. 1392-1398.
  • A. Visioli, Fuzzy logic based set-point weight tuning of PID controllers, IEEE Trans. System, Man, and Cybernetics – Part A: System and Humans, Vol. 29, No. 6, 1999, pp. 587-592.Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
  • S.Y. Chu, C.C. Teng, Tuning of PID controllers based on gain and phase margin specifications using fuzzy neural network, Fuzzy Sets and Systems, Vol. 101(1), 1999, pp. 21-30.
  • G. Zhou and J. D. Birdwell, Fuzzy logic-based PID autotuner design using simulated annealing, Proceedings of the IEEE/IFAC Joint Symposium on Computer-Aided Control System Design, 1994, pp. 67 – 72.
  • D. P. Kwok and F. Sheng, Genetic algorithm and simulated annealing for optimal robot arm PID control, Proc IEEE Conf. Evolutionary Computation, 1994, pp. 707–713.
  • R. A. Krohling and J. P. Rey, Design of optimal disturbance rejection PID controllers using genetic algorithm, IEEE Trans. Evol. Comput., Vol. 5, 2001, pp. 78–82,.
  • P. Wang and D.P. Kwok, Optimal design of PID process controllers based on genetic algorithms, Control Engineer Practice, Vol. 2, No. 4, 1994, pp.641-648.
  • D. H. Kim, Tuning of a PID controller using a artificial immune network model and local fuzzy set, Proceedings of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference, Vol.5, 2001, pp. 2698 – 2703.
  • Y.T. Hsiao, C.L. Chuang, and C.C. Chien, Ant colony optimization for designing of PID controllers, Proceedings of the 2004 IEEE Conference on Control Applications/ International Symposium on Intelligent Control/International Symposium on Computer Aided Control Systems Design, Taipei, Taiwan, , 2004.
  • MM. Eusuf, KE. Lansey, Optimization of water distribution network design using the shuffled frog leaping algorithm. J Water Resour Plan Manage, Vol 129(3), 2003, pp. 210–225.
  • S. Y. Liong, Md. Atiquzzaman., Optimal design of water distribution network using shuffled complex evolution. J Inst Eng, Singapore, Vol 44(1), 2004, pp. 93–107.