  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.
