Infrared Image Segmentation Based on Improved Spotted Hyena Optimizer

Based on the shortcomings of the spotted hyena optimizer (SHO), falling into a local optimal solution or a low-quality solution is easy. In this study, the Lévy flight and simplex method are proposed to improve the SHO(Lévy_SM_SHO). Comparing Lévy_SM_SHO to Lévy flight spotted hyena optimizer (Lévy_SHO), simplex method spotted hyena optimizer (SM_SHO), and spotted hyena optimizer (SHO) on the test function, the experiment proves that the improved algorithm can achieve better optimization results. Finally, the Lévy_SM_SHO algorithm is applied to the infrared image threshold segmentation problem. By crosschecking the segmentation results with the particle swarm optimization algorithm (PSO), we proved that the Lévy_SM_SHO algorithm can achieve better threshold segmentation results.