Enhancing Image Segmentation with Darwinian Grey Wolf Optimizer: A Novel Multilevel Thresholding Approach

This paper presents a novel approach to image segmentation through multilevel thresholding, leveraging the speed and precision of the technique. The proposed algorithm, based on the Grey Wolf Optimizer (GWO), integrates Darwinian principles to address the common stagnation issue in metaheuristic algorithms, which often results in local optima and premature convergence. The search agents are efficiently steered across the search space by a dual mechanism of encouragement and punishment employed by our strategy, thereby curtailing computational time. This is implemented by segmenting the population into distinct groups, each tasked with discovering superior solutions. To validate the algorithm’s efficacy, 9 test images from the Pascal VOC dataset were selected, and the renowned energy curve method was employed for verification. Additionally, Kapur entropy was utilized to gauge the algorithm’s performance. The method was benchmarked against four disparate search algorithms, and its dominance was underscored by achieving the best outcomes in 20 out of 27 cases for image segmentation. The experimental findings collectively affirm that the Darwinian Grey Wolf Optimizer (DGWO) stands as a formidable instrument for multilevel thresholding.