To effectively search for the optimal motion template in dynamic multidimensional space, this paper proposes a novel optimization algorithm, Dynamic Dimension Wrapping (DDW).The algorithm combines Dynamic Time Warping (DTW) and Euclidean distance, and designs a fitness function that adapts to dynamic multidimensional space by establishing a time-data chain mapping across dimensions. This paper also proposes a novel update mechanism,Optimal Dimension Collection (ODC), combined with the search strategy of traditional optimization algorithms, enables DDW to adjust both the dimension values and the number of dimensions of the population individuals simultaneously. In this way, DDW significantly reduces computational complexity and improves search accuracy. Experimental results show that DDW performs excellently in dynamic multidimensional space, outperforming 31 traditional optimization algorithms. This algorithm provides a novel approach to solving dynamic multidimensional optimization problems and demonstrates broad application potential in fields such as motion data analysis.

翻译：为在动态多维空间中有效搜索最优运动模板，本文提出了一种新颖的优化算法——动态维度包裹（DDW）。该算法结合了动态时间规整（DTW）与欧氏距离，通过建立跨维度的时间-数据链映射，设计了一种适应动态多维空间的适应度函数。本文还提出了一种新颖的更新机制——最优维度收集（ODC），结合传统优化算法的搜索策略，使DDW能够同时调整种群个体的维度值及维度数量。由此，DDW显著降低了计算复杂度并提高了搜索精度。实验结果表明，DDW在动态多维空间中表现优异，性能超越了31种传统优化算法。该算法为解决动态多维优化问题提供了一种新思路，并在运动数据分析等领域展现出广阔的应用潜力。