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.
翻译:在基于生物的以人口为基础的美学的拥挤环境中,Salp Swarm优化算法最近出现,并立即获得大量动力。受在领导者之后长期迁移的Salp聚居地的特殊空间空间安排的启发,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,影响关于这一主题的所有后续文件。在本手稿中,我们对SSO进行了批判性审查,突出文献中的所有问题及其对这种算法进行的优化过程的负面影响。我们还提出了SSOS的数学正确版本,名为修正的Salp Swarm优化器(ASO),用以解决所有所讨论的问题。我们根据一系列有针对性的实验对ASSO的绩效进行了基准评估,表明它能够取得比原SO更好的结果。最后,我们进行了广泛的研究,目的是了解SOS及其变异体是否提供了与其他计量经济学相比的好处。实验结果,在SSSO中,SOS不能超越人们所知道的简单高尚的海底物理学界。我们可以建议科学界能够超越SOS。