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

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Abstract

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 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. 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 nonlinear 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, multiparticle improved swarm optimization, normal simplified mean particle swarm optimization algorithm, improved artificial bee colony algorithm, chaotic gray wolf optimization algorithm, hybrid gray wolf optimization algorithm and differential evolution algorithm, random walk gray wolf Optimization algorithm and reinforcement learning gray wolf optimization algorithm, the OPP-IGWO has an improvement on performance by 15.46%, 2.24%, 7.18%, 15.99%, 7.67%, 1.01%, 1.42%, 10.23%, respectively.

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Funding

The work described in this paper is partially supported by the Chinese National Natural Science Foundation (Grant No. 12275208).

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Authors and Affiliations

  1. School of Software Engineering, Xi’an Jiaotong University, Shaanxi, China

    Xiaozhi Du, Ting Yu & Kai Chen

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  1. Xiaozhi Du
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  2. Ting Yu
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  3. Kai Chen
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XD was involved in conceptualization, funding acquisition, read and contributed to the manuscript. TY helped in conceptualization, writing original draft, read and contributed to the manuscript. KC read and contributed to the manuscript.

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Correspondence to Xiaozhi Du.

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Du, X., Yu, T. & Chen, K. An orthodontic path planning method based on improved gray wolf optimization algorithm. Soft Comput 27, 16589–16609 (2023). https://doi.org/10.1007/s00500-023-08924-0

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