GAAOA-Lévy: a hybrid metaheuristic for optimized multilevel thresholding in image segmentation

Image segmentation is a critical task in image processing with applications in various domains, including industry and medicine. However, multilevel thresholding, a widely used segmentation technique, suffers from high computational complexity due to the exhaustive search for optimal thresholds. This paper addresses this challenge by proposing a hybrid Genetic Algorithm-Archimedes Optimization Algorithm (GAAOA), further enhanced with a Lévy flight function (GAAOA-Lévy), to improve efficiency and accuracy in multilevel thresholding. The integration of GA's crossover mechanism strengthens local search capabilities, leading to optimal segmentation with fewer iterations. The proposed algorithm is evaluated using standard benchmark images and compared against well-known optimization techniques. Experimental results demonstrate that GAAOA-Lévy outperforms existing methods in terms of Peak Signal-to-Noise Ratio (PSNR), computational efficiency, and convergence speed, particularly excelling in three-level thresholding while reducing computational costs for higher thresholds.