Salp Swarm Optimization: a Critical Review

Mauro Castelli, Luca Manzoni, Luca Mariot (Corresponding author), Marco S. Nobile, Andrea Tangherloni

Research output: Contribution to journalArticleAcademic

Abstract

In the crowded environment of bio-inspired population-based meta-heuristics, 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 interesting optimization performances. 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 the algorithm. We also propose a mathematically correct version of SSO, named Amended Salp Swarm Optimizer (ASSO) that fixes all the discussed problems. Finally, we benchmark the performance of ASSO on a set of tailored experiments, showing it achieves better results than the original SSO.
Original languageEnglish
Article number2106.01900
Number of pages16
JournalarXiv
Volume2021
DOIs
Publication statusPublished - Jun 2021

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