The Vegetation Evolution (VEGE) algorithm is a metaheuristic optimization method that relies on two stochastic search operations, which may lead to low search efficiency and computational overhead. To address these limitations, we propose two enhanced strategies: (1) an Adaptive Mutation mechanism to dynamically adjust search behavior, and (2) an Elite Exchange strategy to promote information sharing among population groups. The effectiveness of the proposed approach is rigorously validated through ablation studies on the CEC 2017 benchmark suite. Experimental results demonstrate that our improvements significantly accelerate the convergence speed and enhance the solution accuracy of the original VEGE algorithm. This study provides a more efficient variant of VEGE for solving complex optimization problems.