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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 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.

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Correspondence to Bhagyashri Naruka .

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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

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  • DOI: https://doi.org/10.1007/978-81-322-2220-0_20

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  • Print ISBN: 978-81-322-2219-4

  • Online ISBN: 978-81-322-2220-0

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