Abstract
Shuffled frog-leaping algorithm (SFLA) is a recent addition to the family of nature-inspired metaheuristic algorithms (NIMA). SFLA has proved its efficacy in solving intricate and real-world optimization problems. In the present study, we have hybridized SFLA into other well-known metaheuristic algorithm called differential evolution (DE) algorithm to enhance the searching capability as well as to maintain the diversity of population. Hybridization is a growing area of interest in research. The process of hybridization results into a new variant that combines the advantages of two or more metaheuristic algorithms in a judicious manner. In this paper, the new variant is named as differential SFLA (DSFLA). The proposal is implemented and shown its efficacy on the problems of optimization of chemical engineering.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley, Reading (1989)
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)
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)
Karaboga, D.: An idea based on bee swarm for numerical optimization. Technical Report, TR-06, Erciyes University Engineering Faculty, Computer Engineering Department (2005)
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)
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)
Wang, L., Gong, Y.: Diversity analysis of population in shuffled frog leaping algorithm. In: Proceedings of ICSI 2013, Part I, LNCS 7928, pp. 24–31 (2013)
Elbeltagi, E., Hegazy, T., Grierson, D.: A modified shuffled frog-leaping optimization algorithm: applications to project management. Struct. Infrastruct. Eng. 3(1), 53–60 (2007)
Li, X., Luo, J., Chen, M.-R., Wang, N.: An improved shuffled frog-leaping algorithm with extremal optimisation for continuous optimization. Inf. Sci. 192, 143–151 (2012)
Qiusheng, W., Hao, Y., Xiaoyao, S.: A modified shuffled frog leaping algorithm with convergence of update process in local search. In: Proceedings of 2011 International Conference on Instrumentation, Measurement, Computer, Communication and Control, pp. 1016–1019 (2011)
Ali, M., Pant, M.: Improving the performance of differential evolution algorithm using Cauchy mutation. Soft. Comput. 15(5), 991–1007 (2011)
Ali, M., Ahn, C.W., Pant, M.: Multi-level image thresholding by synergetic differential evolution. Appl. Soft Comput. 17, 1–11 (2014)
Ali, M., Ahn, C.W., Pant, M.: A robust image watermarking technique using SVD and differential evolution in DCT domain. Optik Int. J. Light Electron Opt. 125(1), 428–434 (2014)
Jauhar, S.K., Pant, M., Abraham, A.: A novel approach for sustainable supplier selection using differential evolution: a case on pulp and paper industry. Intell. Data Anal. Appl. II, 105–117 (2014)
Thomas, F.E., Himmelblau, D.M., Leon, L.: Optimization of Chemical Processes, 2nd edn. Mcgraw-Hill, New York (1988)
Luus, R., Jaakola, T.: Optimization of nonlinear function subject to equality constraints. Chem. Process Des. Dev. 12, 38G383 (1973)
Deb, K.: An efficient constraint handling method for genetic algorithms. Comput. Methods Appl. Mech. Eng. 18(2–4), 311–338 (2000)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer India
About this paper
Cite this paper
Naruka, B., Sharma, T.K., Pant, M., Sharma, S., Rajpurohit, J. (2015). Differential 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_20
Download citation
DOI: https://doi.org/10.1007/978-81-322-2220-0_20
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2219-4
Online ISBN: 978-81-322-2220-0
eBook Packages: EngineeringEngineering (R0)