针对霜冰算法（rime optimization algorithm, RIME）面对优化问题时求解精度低、收敛速度慢以及易陷入局部最优的问题，提出一种基于多策略融合霜冰算法。首先，采用Tent混沌映射进行种群初始化，提高初始种群多样性；其次，对种群采用增强反向学习机制产生适应度值更优的种群以提高算法收敛速度；最后引入差分进化策略增加种群多样性，避免算法陷入局部最优，提高种群的利用能力以及求解精度。采用CEC 2017测试函数进行数值实验，结果表明：改进算法相较其他对比算法性能更优。最后，将改进算法应用于特征选择问题，选取UCI数据库中的10个数据集进行仿真实验，结果表明：改进算法能够有效提升数据分类准确率。

Aiming at the problems of low solution accuracy, slow convergence speed, and susceptibility to local optima in the rime optimization algorithm (RIME), a multi-strategy fusion-based RIME algorithm has been proposed. Firstly, population initialization was done using Tent chaotic mapping to enhance initial population diversity. Secondly, an enhanced inverse learning mechanism was employed to generate populations with better fitness values, thereby improving the algorithm's convergence speed. Finally, a differential evolution strategy was introduced to increase population diversity, preventing the algorithm from falling into local optima and improving both population utilization and solution accuracy. Numerical experiments were conducted using the CEC 2017 test function, and the results show that the improved algorithm outperforms other comparative algorithms. Lastly, the improved algorithm was applied to feature selection problems, with 10 datasets from the UCI database selected for simulation experiments. The results indicate that the improved algorithm effectively enhances data classification accuracy.