Salp swarm algorithm: Theory, literature review, and application in extreme learning machines

Hossam Faris, Seyedali Mirjalili, Ibrahim Aljarah, Majdi Mafarja, Ali Asghar Heidari

Research output: Chapter in Book/Report/Conference proceedingChapter

13 Citations (Scopus)

Abstract

Salp Swarm Algorithm (SSA) is a recent metaheuristic inspired by the swarming behavior of salps in oceans. SSA has demonstrated its efficiency in various applications since its proposal. In this chapter, the algorithm, its operators, and some of the remarkable works that utilized this algorithm are presented. Moreover, the application of SSA in optimizing the Extreme Learning Machine (ELM) is investigated to improve its accuracy and overcome the shortcomings of its conventional training method. For verification, the algorithm is tested on 10 benchmark datasets and compared to two other well-known training methods. Comparison results show that SSA based training methods outperforms other methods in terms of accuracy and is very competitive in terms of prediction stability.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Pages185-199
Number of pages15
DOIs
Publication statusPublished - 1 Jan 2020
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
Volume811
ISSN (Print)1860-949X

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  • Cite this

    Faris, H., Mirjalili, S., Aljarah, I., Mafarja, M., & Heidari, A. A. (2020). Salp swarm algorithm: Theory, literature review, and application in extreme learning machines. In Studies in Computational Intelligence (pp. 185-199). (Studies in Computational Intelligence; Vol. 811). Springer Verlag. https://doi.org/10.1007/978-3-030-12127-3_11