Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (7): 1938-1943.

• Artificial intelligence •

### Orthodontic path planning based on improved particle swarm optimization algorithm

1. School of Data Science and Technology, North University of China, Taiyuan Shanxi 030051, China
• Received:2019-12-04 Revised:2020-01-02 Online:2020-07-10 Published:2020-06-29
• Supported by:
This work is partially supported by the Shanxi Provincial Key Research and Development Program (201803D31212-1).

### 基于改进粒子群优化算法的牙齿正畸路径规划方法

1. 中北大学 大数据学院, 太原 030051
• 通讯作者: 曾建朝
• 作者简介:徐晓强(1992-),男,山西大同人,硕士研究生,主要研究方向:机器学习、智能计算、数字图像处理;秦品乐(1978-),男,山西长治人,副教授,博士,主要研究方向:机器视觉、大数据、医学影像处理;曾建朝(1963-),男,山西太原人,教授,博士,博士生导师,主要研究方向:演化计算、机器学习。
• 基金资助:
山西省重点研发计划项目（201803D31212-1）

Abstract: Concerning the problem of tooth movement path planning in virtual orthodontic treatment system, a method of tooth movement path planning based on simplified mean particle swarm with normal distribution was proposed. Firstly, the mathematical models of single tooth and whole teeth were established. According to the characteristics of tooth movement, the orthodontic path planning problem was transformed into a constrained optimization problem. Secondly, based on the simplified particle swarm optimization algorithm, a Simplified Mean Particle Swarm Optimization based on the Normal distribution (NSMPSO) algorithm was proposed by introducing the idea of normal distribution and mean particle swarm optimization. Finally, a fitness function with high security was constructed from five aspects:translation path length, rotation angle, collision detection, single-stage tooth moving amount and rotation amount, so as to realize the orthodontic movement path planning. NSMPSO was compared with basic Particle Swarm Optimization (PSO) algorithm, the mean Particle Swarm Optimization (MPSO) algorithm and the Simplified Mean Particle Swarm Optimization with Dynamic adjustment of inertia weight(DSMPSO) algorithm. Results show that on Sphere, Griewank and Ackley, these three benchmark test functions, this improved algorithm tends to be stable and convergent within 50 iteration times, and has the fastest convergence speed and the highest convergence precision. Through the simulation experiments in Matlab, the optimal path obtained by the mathematical models and the improved algorithm is verified to be safe and reliable, which can provide assisted diagnosis for doctors.

CLC Number: