Differential Vectors Empower Snow Ablation Optimizer

The Snow Ablation Optimizer (SAO) algorithm represents a new intelligent search method founded on the sublimation and melting phenomena of snow. It incorporates a dual-population mechanism to balance exploration and exploitation. However, one limitation of SAO is its propensity to become trapped in local optima. Coincidentally, the Differential Evolution (DE) algorithm exhibits strong global search capabilities. In light of this, we propose a fusion algorithm, named DESAO, that combines the strengths of SAO and DE. Our experimental results, conducted on the IEEE CEC2017 benchmark, demonstrate DESAO's superiority of optimization capabilities and rapid convergence rates.