This research proposes a reinforced salp swarm algorithm (SSA) variant with an ensemble mutation strategy and a restart mechanism, which is named CMSRSSSA for short, to enhance exploration and exploitation capacity of SSA and conquer the restriction of a single search mechanism of the SSA in tackling continuous optimization problems. In this variant, an ensemble/composite mutation strategy (CMS) can boost the exploitation and exploration trends of SSA, as well as restart strategy (RS) is capable of assisting salps in getting away from local optimum. To investigate the performance of the proposed optimizer, firstly, IEEE CEC2017 benchmark problems are used to estimate the capability of the presented CMSRSSSA in solving continuous optimization problems in comparison to other advanced algorithms; furthermore, IEEE CEC2011 real-world benchmark problems and constrained engineering optimization problems are also utilized to assess the performance of CMSRSSSA for practical ideas. Experimental and statistical results reveal that the CMSRSSSA outperforms all the competitors, including winners of the related IEEE CEC competition; therefore, it will be able to be treated as a promising method in resolving both constrained and unconstrained optimization problems. For post-publication supports and guides on the idea of the paper, please be in touch with the hosting website: http://aliasgharheidari.com.

本研究提出了一种具有整体突变策略和重启机制的增强型蜂群算法（SSA）变体，简称为CMSRSSSA，以增强SSA的探索和开发能力，克服对SSA单一搜索机制的限制。解决持续优化问题。在此变体中，集成/复合突变策略（CMS）可以促进SSA的开发和探索趋势，而重新启动策略（RS）则可以帮助解决方案摆脱局部最优。为了研究所提出的优化器的性能，首先，与其他高级算法相比，使用IEEE CEC2017基准测试问题来估计所提出的CMSRSSSA解决连续优化问题的能力; 此外，IEEE CEC2011实际基准测试问题和受约束的工程优化问题也被用于评估CMSRSSSA的性能，以获得实用的思路。实验和统计结果表明，CMSRSSSA的表现优于所有竞争对手，包括相关IEEE CEC竞赛的获胜者；因此，它将能够作为解决有约束和无约束优化问题的有前途的方法。有关本文想法的出版后支持和指南，请与托管网站联系：http://aliasgharheidari.com。包括相关IEEE CEC竞赛的获奖者；因此，它将能够作为解决有约束和无约束优化问题的有前途的方法。有关本文想法的出版后支持和指南，请与托管网站联系：http://aliasgharheidari.com。包括相关IEEE CEC竞赛的获奖者；因此，它将能够作为解决有约束和无约束优化问题的有前途的方法。有关本文想法的出版后支持和指南，请与托管网站联系：http://aliasgharheidari.com。