TY - JOUR
T1 - Current studies and applications of shuffled frog leaping algorithm
T2 - A review
AU - Maaroof, Bestan B.
AU - Rashid, Tarik A.
AU - Abdulla, Jaza M.
AU - Hassan, Bryar A.
AU - Alsadoon, Abeer
AU - Mohammadi, Mokhtar
AU - Khishe, Mohammad
AU - Mirjalili, Seyedali
N1 - Funding Information:
Some special thanks go to Kurdistan Institution for Strategic Studies and Scientific Research and the University of Kurdistan Hewler for their help and willingness to conduct this review.
Publisher Copyright:
© 2022, The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE).
PY - 2022/8
Y1 - 2022/8
N2 - 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.
AB - 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.
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UR - https://www.mendeley.com/catalogue/d87a7293-b873-31b6-bb7b-e29fdc931b48/
U2 - 10.1007/s11831-021-09707-2
DO - 10.1007/s11831-021-09707-2
M3 - Review article
AN - SCOPUS:85123485120
SN - 1134-3060
VL - 29
SP - 3459
EP - 3474
JO - Archives of Computational Methods in Engineering
JF - Archives of Computational Methods in Engineering
IS - 5
ER -