Swap Distance Minimization

Swap distance minimization focuses on optimizing arrangements of elements by minimizing the number of pairwise swaps needed to transform one configuration into another. Current research explores this concept across diverse fields, including multi-agent pathfinding (using decentralized algorithms like TP-SWAP), data augmentation for improved machine learning model robustness (e.g., swapping left-right limb data in wearable sensor analysis), and efficient deep learning inference (through techniques like block swapping in SwapNet). These advancements have implications for various applications, from optimizing resource allocation in complex systems to enhancing the privacy and utility of data in sensitive contexts like healthcare.