An orthodontic path planning method based on improved gray wolf optimization algorithm

Automatic tooth arrangement and path planning play 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 insufficient accuracy. To address these issues, this paper proposes innovative method based on the improved gray wolf optimization algorithm, which is called OPP-IGWO. First, model preprocessed to obtain initial pose each segmented built up with oriented bounding box. Next, target determined through ideal dental arch curve optimal jaw principle. Finally, from planned IGWO, mainly three aspects: (1) The greedy idea adopted initialize population interpolation. (2) linear convergence factor traditional algorithm (GWO) replaced a nonlinear factor. (3) We propose position update strategy dynamic weighting approach, introduces learning rate for wolf. experimental results show that our OPP-IGWO outperforms methods. Compared genetic multiparticle swarm optimization, normal simplified mean particle artificial bee colony chaotic hybrid differential evolution random walk Optimization reinforcement has improvement performance by 15.46%, 2.24%, 7.18%, 15.99%, 7.67%, 1.01%, 1.42%, 10.23%, respectively.