A novel mutual aid Salp Swarm Algorithm for global optimization

Abstract

Salp Swarm Algorithm is a new intelligent optimization algorithm. Because of it is fewer control parameters and convenient operation, it has attracted the attention of researchers from all circles. However, due to the lack of complex iterative process, it has some disadvantages, such as low optimization precision and poor population diversity in the late iteration. To solve these problems of Salp Swarm Algorithm, we proposed a Salp Swarm Algorithm based on mutual learning mechanism. In this article, the improved Salp Swarm Algorithm uses the iteration factor of tangent change to update the population position, which balances the global exploration and local development ability of the algorithm. At the same time, the introduction of mutual learning mechanism in the local development stage solves the problem of poor population diversity in the later iteration of Salp Swarm Algorithm, and improves the convergence accuracy of the algorithm. Finally, 23 classical and CEC2014 benchmark functions are used to evaluate the effectiveness of the proposed algorithm. The experimental results show that the improved Salp Swarm Algorithm has better optimization accuracy and stability compared with the algorithm of Salp Swarm, Moth Flame Optimization, Grasshopper Optimization, and Ant Lion Optimization.

Orthogonal pinhole-imaging-based learning salp swarm algorithm with self-adaptive structure for global optimization

“…A social learning tactic inspired by PSO is introduced to accelerate the convergence of the optimizer. In, Zhang et al (2021) introduced a mutual learning mechanism in the exploitation phase of SSA to improve its performance, and used a tangent factor to update the location of the search agent. In, Wang et al (2022c) designed an orthogonal quasi-opposition-based learning structure to avoid the population from falling into local optima.…”