MULTI-OBJECTIVE PARAMETER OPTIMIZATION OF SYNCHRONIZER BASED ON IMPROVED PARTICLE SWARM OPTIMIZATION ALGORITHM

WANG ChunHua; GUO ZhiWei

doi:10.16579/j.issn.1001.9669.2019.02.016

您当前的位置：

首页 >

文章列表页 >

MULTI-OBJECTIVE PARAMETER OPTIMIZATION OF SYNCHRONIZER BASED ON IMPROVED PARTICLE SWARM OPTIMIZATION ALGORITHM

更新时间：2022-09-22

- MULTI-OBJECTIVE PARAMETER OPTIMIZATION OF SYNCHRONIZER BASED ON IMPROVED PARTICLE SWARM OPTIMIZATION ALGORITHM
- Journal of Mechanical Strength Vol. 41, Issue 2, Pages: 356-362(2019)
- 作者机构：
  
  1. 辽宁工程技术大学机械工程学院
  2. 沈阳航空航天大学机电工程学院
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2019.02.016
  CLC：
扫描看全文
WANG ChunHua, GUO ZhiWei. MULTI-OBJECTIVE PARAMETER OPTIMIZATION OF SYNCHRONIZER BASED ON IMPROVED PARTICLE SWARM OPTIMIZATION ALGORITHM. [J]. 41(2):356-362(2019)
DOI：

WANG ChunHua, GUO ZhiWei. MULTI-OBJECTIVE PARAMETER OPTIMIZATION OF SYNCHRONIZER BASED ON IMPROVED PARTICLE SWARM OPTIMIZATION ALGORITHM. [J]. 41(2):356-362(2019) DOI： 10.16579/j.issn.1001.9669.2019.02.016.

摘要

为兼顾变速器中同步器更短同步时间和更高寿命的要求,基于牛顿第二定律和粘着磨损理论、M-B分形接触模型分别建立以同步时间和同步器寿命为目标的数学模型,运用理想点法构造评价函数进而建立以二者为目标的多目标参数优化模型,利用层次分析法确定各目标在多目标优化模型中所占权重值,并借助改进粒子群算法对多目标参数优化模型进行优化求解。结果表明:优化后与优化前相比同步时间减少了8%,同步器寿命增加了9%,二者得到较好的改善。改进粒子群算法的使用使同步器性能得到较好的提升,而理想点法的运用有效的避免了多目标优化中大数量级目标支配优化结果的问题。

Abstract

To meet the requirements of shorter synchronization time and higher life for the synchronizer in transmission, mathematical models aiming at synchronization time and synchronizer life were established separately based on Newton’s second law and adhesive wear theory, M-B fractal contact model. The evaluation function was constructed by ideal point method to establish a multi-objective parameter optimization model with the two objectives. Using the AHP to determine weight value of the target in the multi-objective optimization model, and using the improved particle swarm algorithm to optimize the multi-objective parameter optimization model. The results show that the synchronization time is reduced by 8% and the synchronizer life is increased by 9% compared with that before optimization, the two objectives are improved better. The using of the improved particle swarm algorithm improves the performance of the synchronizer, and the application of the ideal point method effectively avoids the problem that the high order of magnitude dominates the multi-objective optimization.

关键词

同步时间同步器寿命改进粒子群算法多目标

Keywords

Synchronization timeSynchronizer lifeImproved particle swarm optimization algorithmMulti-objective

references

Views

254

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

NUMERICAL CONTROL MILLING PARAMETER OPTIMIZATION ON THE BASIS OF IMPROVED GENETIC ALGORITHM

Related Author

No data

Related Institution

Guang’an Vocational and Technical College

College of Electrical and Information Engineering,Lanzhou University of Technology

⁰