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Salp Swarm Algorithm: Theory, Literature Review, and Application in Extreme Learning Machines

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Book cover Nature-Inspired Optimizers

Part of the book series: Studies in Computational Intelligence ((SCI,volume 811))

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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Author information

Authors and Affiliations

  1. King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Hossam Faris & Ibrahim Aljarah

  2. Institute of Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD, 4111, Australia

    Seyedali Mirjalili

  3. Department of Computer Science, Faculty of Engineering and Technology, Birzeit University, PoBox 14, Birzeit, Palestine

    Majdi Mafarja

  4. School of Surveying and Geospatial Engineering, University of Tehran, Tehran, Iran

    Ali Asghar Heidari

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  1. Hossam Faris
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  2. Seyedali Mirjalili
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  3. Ibrahim Aljarah
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  4. Majdi Mafarja
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  5. Ali Asghar Heidari
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Correspondence to Seyedali Mirjalili .

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Editors and Affiliations

  1. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Seyedali Mirjalili

  2. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Jin Song Dong

  3. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Andrew Lewis

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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: Mirjalili, S., Song Dong, J., Lewis, A. (eds) Nature-Inspired Optimizers. Studies in Computational Intelligence, vol 811. Springer, Cham. https://doi.org/10.1007/978-3-030-12127-3_11

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