ScholarWorks@성균관대학교

초록

To address the issues of uneven population initialization, insufficient individual information interaction, and passive boundary handling in the standard Golden Jackal Optimization (GJO) algorithm, while improving the accuracy and efficiency of multilevel thresholding in artistic image segmentation, this paper proposes an improved Golden Jackal Optimization algorithm (MGJO) and applies it to this task. MGJO introduces a high-quality point set for population initialization, ensuring a more uniform distribution of initial individuals in the search space and better adaptation to the complex grayscale characteristics of artistic images. A dual crossover strategy, integrating horizontal and vertical information exchange, is designed to enhance individual information sharing and fine-grained dimensional search, catering to the segmentation needs of artistic image textures and color layers. Furthermore, a global-optimum-based boundary handling mechanism is constructed to prevent information loss when boundaries are exceeded, thereby preserving the boundary details of artistic images. The performance of MGJO was evaluated on the CEC2017 (dim = 30, 100) and CEC2022 (dim = 10, 20) benchmark suites against seven algorithms, including GWO and IWOA. Population diversity analysis, exploration-exploitation balance assessment, Wilcoxon rank-sum tests, and Friedman mean-rank tests all demonstrate that MGJO significantly outperforms the comparison algorithms in optimization accuracy, stability, and statistical reliability. In multilevel thresholding for artistic image segmentation, using Otsu's between-class variance as the objective function, MGJO achieves higher fitness values (approaching Otsu's optimal values) across various artistic images with complex textures and colors, as well as benchmark images such as Baboon, Camera, and Lena, in 4-, 6-, 8-, and 10-level thresholding tasks. The resulting segmented images exhibit superior peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and feature similarity (FSIM) compared to other algorithms, more precisely preserving brushstroke details and color layers. Friedman average rankings consistently place MGJO in the lead. These experimental results indicate that MGJO effectively overcomes the performance limitations of the standard GJO, demonstrating excellent performance in both numerical optimization and multilevel thresholding artistic image segmentation. It provides an efficient solution for high-dimensional complex optimization problems and practical demands in artistic image processing.

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