Salp swarm algorithm (SSA) is a recently presented algorithm, which is simple in structure and relatively mediocre in its performance. However, the original SSA still has features to be improved because it may face problems in convergence trends or easily being trapped into local optima for more advanced problems. To alleviate this limitation, we propose a new SSA-based method (MCSSA) that performs the chaotic exploitative trends and has a multi-population structure. The new structure can assist SSA in making a more stable tradeoff between global exploration and local exploitation capabilities. First, the exploitation trends and neighborhood searching commands of SSA are enriched using the chaos-assisted exploitation strategy. Next, we arrange a multi-population structure with three sub-strategies to augment the global exploration capabilities of the algorithm. To test the performance of this proposed MCSSA, a set of comprehensive algorithms is used, including 11 other original methods, conventional SSA, and 13 advanced techniques including SCA, SSA, GWO, MFO, WOA, BA, FPA, PSO, ALO, MVO, DE, ABC, CSSA, ESSA, CLSGMFO, LGCMFO, SaDE, jDE, EPSO, ALCPSO, CBA, RCBA, BWOA, CCMWOA, and GA-MPC based on 30 IEEE CEC2017 benchmark functions and 5 IEEE CEC2011 practical test problems. Also, the non-parametric statistics Wilcoxon signed-rank test and Friedman test are also used as an enabling tool to validate the performance of the proposed algorithm. From the result analysis, it can be concluded that the introduced strategy significantly improves the speed of the algorithm converging to the optimal value, and the improvement of the search ability also helps the algorithm to find a better solution than the basic SSA. As a conclusion, it can be said that MCSSA is reliable and efficient in solving complex optimization problems. An online website at https://aliasgharheidari.com supports this research for any guide or info.

Salp群算法（SSA）是最近提出的一种算法，结构简单，性能相对中等。但是，原始的SSA仍然有待改进的功能，因为它可能会遇到收敛趋势方面的问题，或者很容易陷入更高级问题的局部最优中。为了缓解此限制，我们提出了一种新的基于SSA的方法（MCSSA），该方法执行混乱的开发趋势并具有多人口结构。新的结构可以帮助SSA在全球勘探和本地开采能力之间进行更稳定的权衡。首先，利用混沌辅助开发策略丰富了SSA的开发趋势和邻域搜索命令。下一个，我们安排了具有三个子策略的多人口结构，以增强算法的全局探索能力。为了测试此提议的MCSSA的性能，使用了一组综合算法，包括11种其他原始方法，常规SSA和13种先进技术，包括SCA，SSA，GWO，MFO，WOA，BA，FPA，PSO，ALO，MVO ，DE，ABC，CSSA，ESSA，CLSGMFO，LGCMFO，SaDE，jDE，EPSO，ALCPSO，CBA，RCBA，BWOA，CCMWOA和GA-MPC基于30个IEEE CEC2017基准功能和5个IEEE CEC2011实际测试问题。同样，非参数统计Wilcoxon符号秩检验和Friedman检验也被用作验证该算法性能的支持工具。从结果分析 可以得出结论，引入的策略大大提高了算法收敛到最优值的速度，搜索能力的提高也帮助算法找到了比基本SSA更好的解决方案。总之，可以说MCSSA在解决复杂的优化问题方面是可靠且高效的。网址为https://aliasgharheidari.com的在线网站可为任何指南或信息提供支持。