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.
翻译：在基于生物的基于人口的超常医学的拥挤环境中,Salp Swarm优化算法最近出现了,并立即获得了很大的势头。受在领导者之后长期迁移的Salp聚居地的特殊空间安排的启发,这种算法似乎提供了有趣的优化性表现。然而,最初的工作具有一些概念和数学缺陷的特征,影响了随后有关这一主题的所有论文。在这份手稿中,我们对SSO进行了批判性审查,突出了文献中存在的所有问题及其对算法进行的优化进程的负面影响。我们还提出了一个数学正确的SSO版本,名为修正的Salp Swarm优化(ASSO),用以解决所有所讨论的问题。最后,我们把ASO的绩效以一系列定制的实验作为基准,显示它取得了比原SO更好的效果。
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