Color multi-threshold image segmentation is a non-convex, gradient-free global optimization problem. The number of decision variables increases with the number of thresholds, leading to a rapid expansion of the search space and increased computational complexity. To address this problem, this paper proposes a Multi-Mechanism Artificial Lemming Algorithm (MALA). When applied to color multi-threshold image segmentation, the original Artificial Lemming Algorithm (ALA) suffers from an imbalance between exploration and exploitation, excessive reliance on the current best solution, and rigid boundary handling, which may lead to premature convergence and suboptimal threshold selection. MALA integrates three lightweight yet structurally enhancement mechanisms to enhance the stability of the exploration–exploitation process, population-level guidance, and boundary-handling behavior. To verify its general optimization capability, MALA is evaluated on the CEC2017 benchmark suite, where it shows competitive convergence behavior and improved objective values compared with ALA and representative baseline algorithms. Furthermore, segmentation experiments on six benchmark images using Otsu’s criterion show that MALA attains competitive fitness values and generally higher PSNR, SSIM, and FSIM metrics. These results suggest that MALA can serve as a general optimization method with applicability to color multi-threshold image segmentation.
Liang et al. (Sat,) studied this question.