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

  • Hossam Faris
  • Seyedali MirjaliliEmail author
  • Ibrahim Aljarah
  • Majdi Mafarja
  • Ali Asghar Heidari
Part of the Studies in Computational Intelligence book series (SCI, volume 811)


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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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Hossam Faris
    • 1
  • Seyedali Mirjalili
    • 2
    Email author
  • Ibrahim Aljarah
    • 1
  • Majdi Mafarja
    • 3
  • Ali Asghar Heidari
    • 4
  1. 1.King Abdullah II School for Information Technology, The University of JordanAmmanJordan
  2. 2.Institute of Integrated and Intelligent Systems, Griffith University, NathanBrisbaneAustralia
  3. 3.Department of Computer Science, Faculty of Engineering and TechnologyBirzeit UniversityBirzeitPalestine
  4. 4.School of Surveying and Geospatial Engineering, University of TehranTehranIran

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