Image Segmentation Method Based on Improved Grey Wolf Optimizer

Aiming at the drawbacks of slow convergence, proneness to local optima, and insufficient segmentation accuracy of traditional multi-threshold image segmentation algorithms in complex scenarios, an Improved Grey Wolf Optimizer (IGWO) integrated with opposition-based learning strategy and nonlinear dynamic convergence factor is proposed. Specifically, we use improved opposition-based learning for population initialization and iteration optimization. This step helps increase population diversity and speed up convergence. Instead of the traditional linear decreasing convergence factor, we adopt a nonlinear dynamic one. This change achieves an adaptive balance between global exploration and local exploitation of the algorithm. We take the maximum between-class variance (OTSU) as the fitness function and build a multi-threshold segmentation optimization model. We validate IGWO through 6 benchmark test functions and compare it with four advanced swarm intelligence algorithms. The results show that IGWO has obvious advantages in convergence speed, solution accuracy and stability. It also has a strong ability to avoid local optimal solutions. When applied to multi-threshold image segmentation, IGWO produces segmentation regions with clear boundaries and well-preserved details. This algorithm provides a new technical method for efficient and accurate segmentation of complex images. It can be used in fields such as computer vision and communication equipment fault detection.