Salp Swarm Optimization: a Critical Review

TL;DR

This paper critically reviews the Salp Swarm Optimization algorithm, identifies its flaws, proposes a corrected version called ASSO, and demonstrates that ASSO outperforms the original and other metaheuristics, questioning SSO's usefulness.

Contribution

The paper provides a thorough critique of SSO, introduces a mathematically sound variant ASSO, and evaluates its performance, showing SSO's limitations compared to other metaheuristics.

Findings

ASSO outperforms original SSO in benchmark tests.

SSO does not outperform simple well-known metaheuristics.

Scientific community should consider abandoning SSO.

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.