Salp swarm algorithm: a comprehensive survey

• Published in: Neural computing & applications Vol. 32; no. 15; pp. 11195 - 11215
• Main Authors: Abualigah, Laith, Shehab, Mohammad, Alshinwan, Mohammad, Alabool, Hamzeh
• Format: Journal Article
• Language: English
• Published: London Springer London 01.08.2020; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Decision making; Design engineering; Engineering education; Foraging behavior; Heuristic methods; Image processing; Image Processing and Computer Vision; Informatics; Literature reviews; Machine learning; Optimization; Optimization algorithms; Optimization techniques; Organisms; Plankton; Probability and Statistics in Computer Science; Review Article; Salp swarm algorithm; Bio-inspired algorithms; Meta-heuristic optimization algorithms; Optimization problems
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-019-04629-4

This paper completely introduces an exhaustive and a comprehensive review of the so-called salp swarm algorithm (SSA) and discussions its main characteristics. SSA is one of the efficient recent meta-heuristic optimization algorithms, where it has been successfully utilized in a wide range of optimization problems in different fields, such as machine learning, engineering design, wireless networking, image processing, and power energy. This review shows the available literature on SSA, including its variants, like binary, modifications and multi-objective. Followed by its applications, assessment and evaluation, and finally the conclusions, which focus on the current works on SSA, suggest possible future research directions.