针对霜冰优化算法的搜索策略单一化，算法后期搜索开发能力有限，导致算法稳定性不足，提出了一种基于t分布扰动因子和随机差分变异算子的改进策略。在霜冰优化算法的硬刺穿透机制的基础上，引入了t分布扰动因子，局部范围内扩大算法的搜索范围，试图在最优位置周围探索更优的位置。在算法迭代完成后，利用随机差分变异算子对最新更新的粒子位置进行突变，得到更优的粒子。通过测试集CEC2017和CEC2022，与同类算法进行对比实验，发现改进后的霜冰优化算法搜索能力更强，稳定性更好。同时进行了Wilcoxon符号秩检验，验证了算法的显著性差异。利用经典测试函数，从最优值迭代曲线、平均适应度值、第一维度变化趋势和种群历史位置4个维度展开，对算法特征进行了分析。最后应用改进后的霜冰优化算法优化PID（proportional integral derivative）参数进行仿真实验，验证了算法的有效性和适用性。

An improved strategy based on the t-distribution perturbation factor and random differential mutation operator was proposed, in order to address the issue of the RIME(the rime optimization algorithm) algorithm's monolithic search strategy and its limited search and development capabilities in the later stages, resulting in insufficient algorithm stability. Building on the hard-rime puncture mechanism of the RIME algorithm, the t-distribution perturbation factor was introduced to expand the search range within a local scope, aiming to explore superior positions around the optimal location. After the algorithm's iteration was completed, the random differential mutation operator was employed to mutate the newly updated particle positions, yielding better particles. Comparative experiments with similar algorithms on test sets CEC2017 and CEC2022 reveal that the improved RIME algorithm possesses enhanced search capabilities and better stability. Additionally, a Wilcoxon signed-rank test was conducted to verify the significant differences of the algorithm. Using classical test functions, the algorithm's characteristics were analyzed from four dimensions: optimal value iteration curve, average fitness value, first-dimensional variation trend, and population historical positions. Finally, the improved RIME algorithm was applied to optimize PID(proportional integral derivative) parameters in a simulation experiment, validating the algorithm's effectiveness and applicability.