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隐形矫治方案中的牙齿运动路径规划方法研究

  

  1. (西安科技大学计算机科学与技术学院,陕西 西安 710054)
  • 出版日期:2020-08-31 发布日期:2020-08-22
  • 基金资助:
    陕西省自然科学基础研究计划项目(2019JM-162;2019JM-348);西安科技大学博士启动金项目(2019QDJ007)

Research on path planning of teeth movement in invisible orthodontic

  1. (College of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an Shaanxi 710054, China)
  • Online:2020-08-31 Published:2020-08-22
  • Supported by:
    Natural Science Basic Research Plan in Shaanxi Province (2019JM-162; 2019JM-348); Ph.D Research Startup Foundation of Xi’an
    University of Science and Technology (2019QDJ007)

摘要: 针对隐形矫治方案制定过程中传统牙齿运动路径规划方法准确度及效率低下问题,
根据牙颌评价参数提出新的目标函数,再以传统的人工蜂群算法(ABC)为基础,通过外部存储
存放Pareto 解集,然后以改进的Harmonic 距离对Pareto 解集进行更新,从而提高种群的多样
性。随后通过Slerp 球面线性插值以及线性插值获取牙齿运动路径初始值,与人工蜂群算法中
的初始食物源生成方式相结合,生成更好的食物源。通过改进后的人工蜂群算法采用优先级方
案对新目标函数进行优化,得到牙齿的无碰撞运动路径。通过验证本文方法的矫治方案效果,
并与传统目标函数进行比较,结果表明目标函数可以生成更符合临床治疗要求的矫治方案,改
进ABC 算法相比基本ABC 能够获得更优的路径,缩短了矫治阶段数,具有实用价值。

关键词: 隐形矫治, 路径规划, 人工蜂群算法, 多目标优化

Abstract:

Aimed at solving low efficiency and accuracy of path planning of teeth movement in
invisible orthodontic schedule, a new method was proposed. First, a new objective function was
proposed based on the evaluation parameters of teeth and jaws. Based on the traditional artificial bee
colony algorithm (ABC), Pareto solution sets were stored through external storage, and then the
Pareto solution set was updated by the improved Harmonic distance, thus diversifying the population.
Then, the Slerp spherical linear interpolation and linear interpolation were used to obtain the initial
value of the tooth movement path, which was combined with the initial food source generation
method in the artificial colony algorithm to generate a better food source. Finally, the new objective
function was optimized by the priority scheme of the optimized ABC, leading to the collision-free
path for the teeth movement. The experiment showed the effect of the proposed method and compared
it with the traditional objective function. The results show that the proposed objective function can
generate a more suitable schedule for clinical orthodontic. The improved algorithm can result in a
better path and reduce the number of orthodontic stages, and is of practical value.

Key words: invisible orthodontic, path planning, artificial bee colony algorithm, multi-objective optimization