In response to the limitations of traditional Otsu image segmentation—namely high computational overhead and suboptimal segmentation accuracy—this paper proposes an enhanced optimization framework: the Multileader and Fractional‐Order Self‐learning Sparrow Search Algorithm (MLFS‐SSA). By integrating a fractional‐order update mechanism, individual agents retain and leverage historical state information during position updates, facilitating escape from local optima and accelerating convergence. Furthermore, a multileader memory strategy is designed to reducing population search pattern redundancy and improving solution diversity. A self‐learning mutation mechanism and Lévy flight perturbation are incorporated to reinforce local exploitation and global exploration, respectively. The proposed algorithm is applied to solve the Otsu multithreshold optimization problem, using the interclass variance criterion as the objective function. Extensive experiments on benchmark images demonstrate that MLFS‐SSA significantly outperforms classical algorithms in terms of segmentation accuracy and computational efficiency. Ablation studies further confirm the individual contributions of each strategy to the algorithm’s overall performance.
Huang et al. (Thu,) studied this question.