A study of improved salp swarm algorithm in threshold segmentation of two-dimensional OTSU images

Aiming at the deficiencies of the Slap Swarm Algorithm in terms of solution accuracy, convergence speed and global search capability, an improved Slap Swarm Algorithm is proposed. Firstly, an improved Tent chaotic sequence is used for population initialization; secondly, adaptive weight factor and Levy flight strategy are used to update the leader position; meanwhile, non-uniform Gaussian variation operator is used to update the follower position. In the experimental results, it is shown that the improved algorithm has higher accuracy as well as convergence; in the application, the selection of the optimal average fitness value (image segmentation threshold) is faster and more accurate, and the segmentation threshold is more appropriate.