To address the key challenges in shortest path planning for known static obstacle maps—such as the tendency to converge to local optima in U-shaped/narrow obstacle regions, unbalanced computational efficiency, and suboptimal path quality—this paper presents an enhanced Artificial Lemming Algorithm (DMSALAs). The algorithm integrates a dynamic adaptive mechanism, a hybrid Nelder–Mead method, and a localized perturbation strategy to improve the search performance of ALAs. To validate DMSALAs efficacy, we conducted ablation studies and performance comparisons on the IEEE CEC 2017 and CEC 2022 benchmark suites. Furthermore, we evaluated the algorithm in mobile robot path planning scenarios, including simulated grid maps (10 × 10, 20 × 20, 30 × 30, 40 × 40) and a real-world experimental environment built by our team. These experiments confirm that DMSALAs effectively balance optimization accuracy and practical applicability in path planning problems.