This study proposes a new flower pollination algorithm by incorporating the improved teaching-learning-based optimization strategy and dynamic Gaussian mutation to enhance the optimization performance. The algorithm first speeds the convergence through the promotion effect between the optimal individual and other individuals obtained by the improved teaching factor in the teaching mechanism. At the same time, the mutual learning mechanism between individuals is adopted to maintain the diversity of the population, thereby improving the optimization accuracy. Then, when it is detected that the algorithm falls into prematurity, the dynamic Gaussian mutation is carried out on the middle individuals of the population to increase the differences between individuals. In this way, it avoids the prematurity of the algorithm and then improves the comprehensive optimization ability. The optimization results of 16 standard functions are checked by the nonparametric statistical test to prove the effectiveness of the algorithm. Compared with other improved pollination algorithms, this algorithm has significant advantages. Finally, the new algorithm is applied to solve the application problems of telescopic rope, and good optimization results are achieved.