Multi‐Strategy Snow Ablation Optimizer for Global Optimization

The snow ablation optimizer (SAO) is a novel meta‐heuristic algorithm inspired by the natural processes of snow melting and sublimation. SAO employs a dual‐population dynamic adjustment mechanism to balance exploration and exploitation, demonstrating significant performance in global optimization. However, when dealing with complex optimization problems, the SAO algorithm tends to suffer from low convergence accuracy and premature convergence. To address these issues, this paper proposes a multi‐strategy snow ablation optimizer (MSSAO). Firstly, it adopts an external archive mechanism to prevent convergence to local optima. Secondly, it utilizes an adaptive t ‐distribution perturbation strategy to achieve a balance between exploration and exploitation. Finally, it employs a mirror reflection learning strategy to maintain population diversity. Comprehensive experiments conducted on the CEC2017 and CEC2022 test suites demonstrate that MSSAO significantly enhances both global optimization performance and convergence accuracy. Furthermore, its effectiveness in engineering optimization is demonstrated through a DC motor control system.