Salp Swarm Optimization: A critical review. (1st March 2022)

• Record Type: Journal Article
• Authors: Castelli, Mauro; Manzoni, Luca; Mariot, Luca; Nobile, Marco S.; Tangherloni, Andrea
• Is Part Of: Expert systems with applications. Volume 189(2022)
• Publication Date: 2022-03-01
• Subjects: Metaheuristics -- Global optimization -- Bound constrained optimization -- Shift invariant functions; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.116029
• Languages: English
• ISSNs: 0957-4174

Abstract

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. Highlights: Critical analysis of the widely-used salp optimization algorithm (SSO). Definition of an amended version of SSO that fixed SSO's structural bias. Extensive experimental phase using the CEC 2017 benchmark suite. Results show that SSO does not provide any advantage w.r.t. existing heuristics. Metaphor-based approach to design metaheuristics may introduce bias.