An Orthodontic Path Planning Method Based on Improved Gray Wolf Optimization Algorithm

Abstract Automatic tooth arrangement and path planning plays an essential role in computer-aided orthodontic treatment. However, state-of-the-art methods have some shortcomings: low efficiency, excessive cost of displacements or collisions and insufficient accuracy. To address these issues, this paper proposes an innovative orthodontic path planning method based on the improved gray wolf optimization algorithm, which is called OPP-IGWO. First, the tooth model is preprocessed to obtain initial pose in which each tooth is segmented and built up with an oriented bounding box (OBB). Next, the target pose of each tooth is determined through the ideal dental arch curve and the optimal jaw principle. Finally, the path from the initial pose to the target pose of each tooth is planned based on IGWO, which is improved mainly from three aspects: (1) The greedy idea is adopted to initialize the gray wolf population based on dental interpolation. (2) The linear convergence factor in the traditional gray wolf optimization algorithm (GWO) is replaced with a non-linear convergence factor. (3) We propose a position update strategy based on a dynamic weighting approach, which introduces a learning rate for each gray wolf. The experimental results show that our OPP-IGWO method outperforms the state-of-the-art methods. Compared with improved genetic algorithm (IGA), normal simplified mean particle swarm optimization algorithm (NSMPSO), multiparticle improved swarm optimization (Mutil-IPSO) and improved artificial bee colony algorithm (IABC), the OPP-IGWO has an improvement on performance by 14.22%, 11.46%, 6.32% and 5.27% respectively.