In recent years, nature-inspired optimization algorithms have demonstrated powerful performance in continuous and combinatorial optimization problems. Traditional optimization algorithms often suffer from problems such as getting trapped in local optima, slow convergence speed, or insufficient search accuracy when dealing with high-dimensional, multi-modal complex functions. This paper proposes a novel swarm intelligence optimization algorithm—Adaptive Sand Rat Optimization (ASRO)—inspired by the foraging behavior, social cooperation, and risk-avoidance jumping mechanisms of gerbils in their natural environment. The algorithm achieves an organic combination of global search and local exploitation by introducing strategies such as dynamic environment perception, swarm cooperation and information sharing, nonlinear jumping, multi-level search, and adaptive convergence control. This paper presents the mathematical modeling, detailed process, and convergence control formula of the algorithm, and verifies it on several typical optimization test functions. Experimental results show that ASRO outperforms traditional swarm intelligence algorithms in terms of global search capability, local search accuracy, and convergence stability.