# Dynamic Weight and Mapping Mutation Operation-Based Salp Swarm Algorithm for Global Optimization
###### Abstract  
The salp swarm algorithm imitates the swarm behavior of salps during navigation and hunting that has been proven the superiority of search for best solution. However, although it has sufficient global search ability, it is still worth paying attention to problems of falling into local optima and lower convergence accuracy. This paper proposes some improvements to the salp swarm algorithm that are based on a nonlinear dynamic weight and the mapping mutation operation. Firstly, the nonlinear dynamic weight is helpful for further optimizing the transition from exploration to exploitation and alleviating the local optima stagnation phenomena. Secondly, utilizing a mapping mutation operation can increase the diversity of followers in algorithm, to avoid getting trapped into the local optima during the search and provide a better optimal solution. The proposed algorithm is characterized by a stronger global optimization capability and high convergence accuracy. Finally, to confirm the effectiveness of the proposed algorithm, comparative experiments based on other well-known swarm-based algorithms and each improvement for the original algorithm are conducted. The quantitative results and convergence curves among several algorithms demonstrate that the enhanced algorithm with the nonlinear dynamic weight and mapping mutation operation can outperform the original algorithm. s +
Footnote †: journal: Applied Sciences  
article  
[color=gray!20,][color=gray! the foraging process. There are some classic algorithms based on swarm intelligence optimization technology, for instance, dolphin echolocation (DEL) [7; 8], firefly algorithm (FA) [9; 10], and bat algorithm (BA) [11]. Furthermore, FA mimics the mating behavior of fireflies in nature, while DEL and BA simulate dolphins and navigating bats, respectively. Moreover, some latest developed and effective algorithms are electric fish optimization (EFO) [12], which simulates the predation and communication behaviors of electric fish, whale optimization algorithm (WOA) [13], which imitates the hunting process of humpback whales, and antilo optimizer (ALO) [14], which simulates the foraging of antlions in nature. In 2017, [PERSON] released the salp swarm algorithm (SSA) [15], which mimics the salp swarm. Recently, SSA has received extensive attention from researchers in many fields, including the feature selection for image classification [16], the variable speed wind generators [17], and engineering optimization problems [18]. SSA mainly imitates the swarm behaviors of salp chains navigating and foraging in the ocean. The categories of salp chains are leaders and followers. Generally, leaders play the role of leading the salp chains in the swarm, and at the same time, leaders move towards the food source. Followers are guided by the leaders through their chained bahaviors to move. The salp chains utilize the interactive behavior of leaders and followers to prey quickly and accurately in the ocean space. To alleviate the local optimum stagnation and promote convergence accuracy, [PERSON] et al. [19] introduced the adaptive control parameter, and the best gray wolf algorithm dominates the position update stage of followers and the last stage of the population, respectively. The improved SSA enhances the local exploitative performance of the followers and helps the population search for the target faster. To solve the phenomena of local stagnation, [PERSON] et al. [18] utilized chaotic initialization tactics and meristic accommodative population. In addition, a simulated annealing mechanism ground on meristic disturbance is utilized to improve the competence to escape from the local optimum. [PERSON] et al. [20] primarily engineered an update mechanism for leaders over logistic mapping to boost the diversity of the population and exploited a dynamic learning dependent follower renewal mechanism to strengthen the global search capability of the algorithm. [PERSON] et al.