Ensemble mutation-driven salp swarm algorithm with restart mechanism: Framework and fundamental analysis

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.

Automated summary

The proposed CMSRSSSA algorithm effectively enhances the exploration and exploitation capacity of the salp swarm algorithm, outperforming competitors in solving a variety of optimization problems. The ensemble mutation strategy and restart mechanism make CMSRSSSA a promising method for both constrained and unconstrained optimization tasks.