Skip to main content

Accelerated Shuffled Frog-Leaping Algorithm

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 336)


Shuffled frog-leaping algorithm (SFLA) is a recent addition to the family of stochastic search methods that mimic the social and natural behavior of species. SFLA combines the advantages of local search process of particle swarm optimization (PSO) and mixing of information of the shuffled complex evolution. The basic idea behind modeling of such algorithms is to achieve near to global solutions to the large-scale optimization problems and complex problems which cannot be solved using deterministic or traditional numerical techniques. In this study, the searching process is accelerated using golden section-based scaling factor and the constraints are handled by the penalty functions. Penalty functions are used to find the optimal solution for restrained optimization problems in the feasible region of the total search space. The resulting algorithm is named as Accelerated-SFLA. The proposal is implemented to solve the problem of optimal selection of processes. The results illustrate the efficacy of the proposal.


  • Shuffled frog-leaping algorithm
  • Constrained optimization
  • Memetic
  • Swarm intelligence

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-81-322-2220-0_15
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   219.00
Price excludes VAT (USA)
  • ISBN: 978-81-322-2220-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   279.99
Price excludes VAT (USA)
Fig. 1
Fig. 2


  1. Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence Through Simulated Evolution. Wiley, New York (1966)

    MATH  Google Scholar 

  2. Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley, Reading (1989)

    MATH  Google Scholar 

  3. Rechenberg, I.: Evolutions strategie: optimierung technischer systeme nach prinzipien der biologischen evolution. Ph.D. Thesis, Technical University of Berlin, Department of Process Engineering (1971)

    Google Scholar 

  4. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceeding of IEEE International Conference on Neural Networks, pp. 1942–1948, Perth, Australia. IEEE Service Center, Piscataway, NJ (1995)

    Google Scholar 

  5. Price, K., Storn, R.: Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report, International Computer Science Institute, Berkley (1995)

    Google Scholar 

  6. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22(3), 52–67 (2002)

    CrossRef  MathSciNet  Google Scholar 

  7. Karaboga, D.: An idea based on bee swarm for numerical optimization. Technical Report, TR-06, Erciyes University Engineering Faculty, Computer Engineering Department (2005)

    Google Scholar 

  8. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39, 459–471 (2007)

    CrossRef  MATH  MathSciNet  Google Scholar 

  9. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern B 26(1), 29–41 (1996)

    CrossRef  Google Scholar 

  10. Eusuff, M., Lansey, K.E.: Optimization of water distribution network design using the shuffled frog leaping algorithm. Water Resour. Plan. Manage. 129(3), 210–225 (2003)

    CrossRef  Google Scholar 

  11. Deb, K.: An efficient constraint handling method for genetic algorithms. Comput. Methods Appl. Mech. Eng. 186, 311–338 (2000)

    CrossRef  MATH  Google Scholar 

  12. Kiefer, J.: Sequential minimax search for a maximum. Proc. Am. Math. Soc. 4, 502–506 (1953)

    CrossRef  MATH  MathSciNet  Google Scholar 

  13. Floudas, C.: Nonlinear and Mixed-Integer Optimization. Oxford University Press, New York (1995)

    MATH  Google Scholar 

  14. Srinivas, M., Rangaiah, G.P.: Differential evolution with tabu list for solving nonlinear and mixed-integer nonlinear programming problems. Ind. Eng. Chem. Res. 46(22), 7126–7135 (2007)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Shweta Sharma .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer India

About this paper

Cite this paper

Sharma, S., Sharma, T.K., Pant, M., Rajpurohit, J., Naruka, B. (2015). Accelerated Shuffled Frog-Leaping Algorithm. In: Das, K., Deep, K., Pant, M., Bansal, J., Nagar, A. (eds) Proceedings of Fourth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 336. Springer, New Delhi.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2219-4

  • Online ISBN: 978-81-322-2220-0

  • eBook Packages: EngineeringEngineering (R0)