The rime optimization algorithm (RIME) is an intelligent optimization algorithm inspired by the natural growth process of rime. It demonstrates strong optimization capability by employing a soft rime strategy for global search and a hard rime strategy for local exploitation. However, RIME suffers from slow convergence and a tendency to fall into local optima during applications. To address these issues, this paper proposes an improved rime optimization algorithm (IRIME). First, a dynamic centroid guidance strategy is introduced in the early stages of the algorithm, significantly enhancing convergence speed. Second, an improved differential mutation operator is incorporated into the later iterations to effectively reduce the risk of the algorithm becoming trapped in local optima. Additionally, a novel centroid boundary adjustment strategy is designed to enable collaborative optimization of accuracy and efficiency by deeply exploiting population information. Experiments conducted using the CEC2017 benchmark set demonstrate that IRIME outperforms PPSO, AGWO, HPHHO, RIME, and SRIME in optimization performance. Furthermore, IRIME is applied to the three-dimensional path planning problem for UAVs. The results indicate that IRIME provides substantial improvements in solution quality, convergence stability, and computational efficiency, offering an effective solution for complex engineering optimization problems.