Skip to main content
SpringerLink
Log in

Accelerated Shuffled Frog-Leaping Algorithm

  • Conference paper
  • First Online:
Book cover Proceedings of Fourth International Conference on Soft Computing for Problem Solving

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

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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)

    Article  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)

    Article  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)

    Article  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)

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

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

    Article  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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Amity University, Jaipur, Rajasthan, India

    Shweta Sharma, Tarun Kumar Sharma, Jitendra Rajpurohit & Bhagyashri Naruka

  2. IIT Roorkee, Roorkee, India

    Millie Pant

Authors
  1. Shweta Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tarun Kumar Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Millie Pant
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jitendra Rajpurohit
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bhagyashri Naruka
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shweta Sharma .

Editor information

Editors and Affiliations

  1. Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Kedar Nath Das

  2. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  3. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, India

    Millie Pant

  4. South Asian University, New Delhi, India

    Jagdish Chand Bansal

  5. Department of Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya Nagar

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. https://doi.org/10.1007/978-81-322-2220-0_15

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-2220-0_15

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2219-4

  • Online ISBN: 978-81-322-2220-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation