针对人工兔优化（artificial rabbits optimization, ARO）算法具有低精度、慢收敛、容易陷入局部最优等缺点，提出一种镜面反射学习ARO（specular reflection learning ARO, SRL-ARO）算法。在SRL-ARO算法中，为了搜索过程从探索到开发的平稳过渡，设计了一种自适应能量递减因子；在开发阶段嵌入全局最优信息以加快收敛和改善解的精度；引入镜面反射学习策略以增强群体多样性和跳出局部最优。选取6个基准测试函数进行实验，结果表明，与其他智能优化算法相比，SRL-ARO算法的收敛精度和收敛速度均有明显的提高。将SRL-ARO算法用于求解特征选择问题，比较结果显示，所提出的算法在总体性能上要优于其他方法。

The artificial rabbits optimization(ARO) has some disadvantages such as low precision, slow convergence, and easy to fall into local optima. A specular reflection learning based ARO (SRL-ARO) algorithm was proposed. In SRL-ARO, an adaptive energy decreasing factor was designed to ensure a smooth transition of the algorithm from exploration to exploitation. The global optimum information was embedded in the exploitation phase to speed up convergence and improve solution precision. The specular reflection learning strategy was introduced to enhance the population diversity and jump out of local optima. Six benchmark test functions were selected for experiments. The results show that the proposed SRL-ARO algorithm shows significant improvement in terms of accuracy and convergence compared with other intelligent optimization algorithms. Finally, SRL-ARO was employed to deal with the feature selection problem. The compared findings indicate that the total performance of the proposed SRL-ARO is superior to other methods.