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

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

doi:10.1007/978-3-030-12127-3_11

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 proceeding › Chapter › peer-review

157 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 language	English
Title of host publication	Studies in Computational Intelligence
Publisher	Springer Verlag
Pages	185-199
Number of pages	15
DOIs	https://doi.org/10.1007/978-3-030-12127-3_11
Publication status	Published - 1 Jan 2020
Externally published	Yes

Publication series

Name	Studies in Computational Intelligence
Volume	811
ISSN (Print)	1860-949X

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities
SDG 12 Responsible Consumption and Production
SDG 13 Climate Action
SDG 17 Partnerships for the Goals

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10.1007/978-3-030-12127-3_11

Cite this

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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.",

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AU - Mirjalili, Seyedali

AU - Aljarah, Ibrahim

AU - Mafarja, Majdi

AU - Heidari, Ali Asghar

PY - 2020/1/1

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BT - Studies in Computational Intelligence

PB - Springer Verlag

ER -

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

Abstract

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UN SDGs

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