Salp Swarm Optimization: A critical review

Mauro Castelli, Luca Manzoni, Luca Mariot, Marco S. Nobile, Andrea Tangherloni

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In the crowded environment of bio-inspired population-based metaheuristics, the Salp Swarm Optimization (SSO) algorithm recently appeared and immediately gained a lot of momentum. Inspired by the peculiar spatial arrangement of salp colonies, which are displaced in long chains following a leader, this algorithm seems to provide an interesting optimization performance. However, the original work was characterized by some conceptual and mathematical flaws, which influenced all ensuing papers on the subject. In this manuscript, we perform a critical review of SSO, highlighting all the issues present in the literature and their negative effects on the optimization process carried out by this algorithm. We also propose a mathematically correct version of SSO, named Amended Salp Swarm Optimizer (ASSO) that fixes all the discussed problems. We benchmarked the performance of ASSO on a set of tailored experiments, showing that it is able to achieve better results than the original SSO. Finally, we performed an extensive study aimed at understanding whether SSO and its variants provide advantages compared to other metaheuristics. The experimental results, where SSO cannot outperform simple well-known metaheuristics, suggest that the scientific community can safely abandon SSO.
Original languageEnglish
Article number116029
Pages (from-to)1-12
Number of pages12
JournalExpert Systems with Applications
Volume189
Early online date16 Oct 2021
DOIs
Publication statusE-pub ahead of print - 16 Oct 2021

Keywords

  • Metaheuristics
  • Global optimization
  • Bound constrained optimization
  • Shift invariant functions

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