Current Studies and Applications of Shuffled Frog Leaping Algorithm: A Review

TL;DR

This review paper summarizes the development, modifications, hybridizations, and applications of the Shuffled Frog Leaping Algorithm (SFLA), highlighting recent advances and proposing a framework for analyzing its use across various domains.

Contribution

It provides a comprehensive overview of SFLA's evolution, recent improvements, hybrid methods, and applications, offering a structured analysis framework for future research.

Findings

SFLA has been widely applied in engineering problems.

Recent modifications and hybridizations have improved SFLA's performance.

A proposed operational framework aids in analyzing SFLA's diverse applications.

Abstract

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