Dynamic Dimension Wrapping (DDW) Algorithm: A Novel Approach for Efficient Cross-Dimensional Search in Dynamic Multidimensional Spaces

TL;DR

The paper introduces Dynamic Dimension Wrapping (DDW), an innovative optimization algorithm that efficiently searches dynamic multidimensional spaces by combining DTW and Euclidean distance, with adaptive mechanisms for improved accuracy and reduced complexity.

Contribution

It presents DDW, a novel algorithm integrating DTW and Euclidean distance with an adaptive update mechanism for dynamic multidimensional optimization.

Findings

DDW outperforms 31 traditional algorithms in dynamic spaces.

DDW reduces computational complexity significantly.

Demonstrates broad application potential in motion data analysis.

Abstract

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.