摘要
提出将一种改进的粒子群优化算法应用于汽车减振器的优化中。该算法在标准粒子群算法的基础上引入了一个概率参数,使得粒子群优化算法的全局优化能力和收敛速度得到显著改善,并利用该算法对汽车减振器的主要参数进行了优化。结果表明,对减振器参数优化后,明显改善了汽车减振器压缩行程和复原行程的阻尼特性,提高了汽车的平顺性。
Based on the study of the performance of automotive shock absorber,an Improved Particle Swarm Optimization（IPSO）algorithm for automotive shock absorber optimization is proposed.This algorithm introduces a probability parameter into the standard PSO algorithm,thus significantly improves the global optimization and convergent rate in comparison with standard PSO.Using this IPSO algorithm,the main parameters of automotive shock absorber are optimized.Results show that this method can significantly improve the damping performance of automotive shock absorber in compression and recovery processes and the ride comfort performance.
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2010年第2期341-345,共5页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金项目(50475011)
关键词
车辆工程
减振器
IPSO算法
SPSO算法
参数优化
vehicle engineering
shock absorber
IPSO algorithm
SPSO algorithm
parameter optimization
