# Dynamic Weight and Mapping Mutation Operation-Based Salp Swarm Algorithm for Global Optimization
###### Abstract
[21] proposed a novel SSA called OBSSA, which relies on an opposition-based learning strategy to improve the capacity to circumvent local optima. The algorithm consists of two phases: in the first phase, OBL is used to augment the initialized population; in the second phase, OBL is applied to the population update process in each iteration. [PERSON] et al. [22] proposed a new algorithm named SSAPSO by mixing PSO with SSA, which enhanced the exploration and utilization efficiency of the algorithm. The base construction of the SSA algorithm is altered with the improvement of the update stage of the population location. This revision incorporates the update regime of PSO into the main construction of SSA. This consolidation creates more flexibility for SSA in exploring populations, ensuring that populations are diverse and can attain optimal values rapidly. In light of the No Free Lunch (NFL) theorem [23; 24], no algorithm can settle all optimization problems. Similar to other swarm optimization algorithms, SSA has local optima stagnation phenomena and low convergence accuracy. Although many researchers have made many improvements to the classic SSA, SSA can still be improved to enhance its ability to moderate the local optima stagnation and convergence accuracy. Therefore, this paper uses some improvements to boost the global search capacity and alleviate premature convergence. In response to the above-mentioned problems suffered by traditional SSA, this presented paper proposes two methods for leaders and followers. First, the application of the nonlinear dynamic weight strategy to further optimize the transfer control parameter in leaders. In the optimization algorithms based on meta-heuristics, there are two key tasks, called exploration and exploitation [25; 26]. In the exploration task, SSA uses the leaders' behavior toward food sources to find the best areas. In the exploitation task, however, followers use their own information interaction and the leaders' guidance to realize the behavior of searching for the first-rank solution in the most promising area. TraditionalSSA uses nonlinear control strategy to achieve the transition from global exploration to local exploitation, and good results have been obtained. In this paper, a nonlinear control parameter, namely dynamic weight, is used to fine-tune the transfer control parameter to achieve the secondary control of the transition. Second, in order to increase the dimensionality of the followers during the position update and to avoid getting trapped into the local optima, a mapping mutation operation is introduced. When followers find that they cannot search for a better position during the search process, the mapping mutation operation can keep the algorithm from getting trapped into the local optima by providing a random jump. By implementing these two improvements to the traditional SSA, the improved SSA named WMSSA can more smoothly transfer from global exploration to local exploitation, and can make full use of the advantages brought by the mapping mutation operation, which can expand the population variety of followers and promote the algorithm to escape local optima. Finally, to validate the performance of the improved SSA, some comparative experiments based on several effective and classical swarm optimization algorithms and 10 benchmark functions are implemented. The statistics show that proposed WMSSA outperforms classical SSA and other swarm-based optimization algorithms. This article is divided into sections. Section 2 shows the classical SSA and its literature review. The detailed mathematical type of the modified SSA is presented in Section 3. To assess the overall effectiveness of the modified SSA, various kinds of simulation experiments and analysis that compare the improved SSA with other four celebrated swarm-based optimization algorithms (containing the classical SSA) and compare classical SSA with the corresponding algorithms for each improvement are provided in Section 4. Section 5 shows the final conclusion and the future research direction.