  Shuffled Frog Leaping Algorithm (SFLA) is one of the most widespread
algorithms. It was developed by Eusuff and Lansey in 2006. SFLA is a
population-based metaheuristic algorithm that combines the benefits of memetics
with particle swarm optimization. It has been used in various areas, especially
in engineering problems due to its implementation easiness and limited
variables. Many improvements have been made to the algorithm to alleviate its
drawbacks, whether they were achieved through modifications or hybridizations
with other well-known algorithms. This paper reviews the most relevant works on
this algorithm. An overview of the SFLA is first conducted, followed by the
algorithm's most recent modifications and hybridizations. Next, recent
applications of the algorithm are discussed. Then, an operational framework of
SLFA and its variants is proposed to analyze their uses on different cohorts of
applications. Finally, future improvements to the algorithm are suggested. The
main incentive to conduct this survey to provide useful information about the
SFLA to researchers interested in working on the algorithm's enhancement or
application
