Differential shuffled frog-leaping algorithm

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.