改进粒子群算法的行星齿轮系统多目标优化研究

徐向阳; 韩洵; 艾星; 傅嵩

doi:10.13433/j.cnki.1003-8728.20180068

改进粒子群算法的行星齿轮系统多目标优化研究

doi: 10.13433/j.cnki.1003-8728.20180068

1.
重庆交通大学机电与车辆工程学院, 重庆 400074

基金项目:

国家自然科学基金项目（51775072）、长安大学公路养护装备国家工程实验室开放基金项目（310825161104）及重庆市人工智能技术创新重大主题专项重点研发项目（cstc2017rgzn-zdyf0027）资助

详细信息

作者简介:
徐向阳(1981-),副教授,博士,研究方向为齿轮系统动力学,24910304@qq.com

计量
- 文章访问数: 352
- HTML全文浏览量: 31
- PDF下载量: 122
- 被引次数: 0
出版历程
- 收稿日期: 2017-10-15
- 刊出日期: 2018-09-05

Research on Multi-objective Optimization of Planetary Gear System with Improved Particle Swarm Optimization

1.
School of Mechanotronics & Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China

摘要

摘要: 在行星齿轮多目标优化中，传统粒子群算法（PSO）与自适应权重粒子群算法（APSO）在复杂约束下不易收敛或易陷入局部最优。为此，提出改进的自适应权重粒子群算法（D-APSO）并进行行星齿轮高功率密度的多目标优化设计，以最小体积、最大传动效率和最小中心距为多目标优化函数，综合考虑行星齿轮传动的边界协调条件，利用惩罚函数法处理约束条件，对目标进行D-APSO算法下的优化计算。结果表明：D-APSO算法在优化求解效果和速度上明显优于传统PSO算法和APSO算法，在满足行星齿轮系统承载性能的条件下，使行星齿轮系统具有更小的体积及中心距，并表现出更优的传动效率。
- 行星齿轮系统 /
- 自适应权重 /
- 粒子群算法 /
- 多目标优化
Abstract: In the multi-objective optimization of planetary gears, traditional particle swarm optimization (PSO) and adaptive weight particle swarm optimization (APSO) algorithms are not easy to converge or fall into local optimum under complex constraints. An improved adaptive weight particle swarm optimization (D-APSO) method is proposed and used to a multi-objective optimization design of the high power density of the planetary gear. With the minimum volume, maximum transmission efficiency and minimum central moment as the multi-objective optimization function, the penalty function method is used to deal with the constraint condition and the planetary gear boundary condition, and the objective is optimized by D-APSO algorithm. The calculation results show that the D-APSO algorithm is superior to the traditional PSO and the APSO algorithms in optimization speed and solution accuracy. While satisfying the carrier performance of the planetary gear system, the D-APSO algorithm can obtain smaller volume, smaller central distance and better transmission efficiency of the planetary gear system.
- planetary gear system /
- adaptive weight /
- particle swarm optimization /
- multi-objective optimization

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参考文献(15)

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