基于黏菌优化算法的机械传动行星轮系多目标优化设计
Multi-objective optimization design of mechanical transmission planetary gear train based on slime mold optimization algorithm
李崑 1钱谦1
作者信息
- 1. 昆明理工大学信息工程与自动化学院,云南昆明 650500;昆明理工大学云南省计算机技术应用重点实验室,云南昆明 650500
- 折叠
摘要
为了优化机械传动中的关键部件行星轮系设计模型,提出改进的黏菌优化算法.该算法通过加权聚合学习机制,使黏菌个体在搜索空间中能够更好地学习和利用其他个体的优秀信息,从而提高收敛速度和优化精度.将行星轮系的传动比、齿轮齿数、模数等关键参数作为优化变量,以变量之间所满足的关系为约束条件,以传动效率、体积、噪音等性能指标作为优化目标.通过构建合适的适应度函数,将行星轮系设计模型优化问题转化为一个多目标优化问题,并将该算法与9个对比算法在函数测试集和行星轮设计模型上进行试验验证.研究结果表明:基于加权聚合学习机制的黏菌优化算法进行行星轮系设计优化效果显著,具有收敛速度快、优化精度高、稳定性好等优点,不仅能够在较短时间内找到全局最优解,而且能够提供更加稳定和可靠的优化结果.
Abstract
Amid at crucial component in mechanical transmission in mechanical transmission,a planetary gear system design optimization model based on improved slime mold optimization algorithm was proposed.The weighted aggregation learning mechanism was introduced,the algorithm enables slime mold individuals to better learn and utilize excellent information from other individuals in the search space,thereby accelerating convergence speed and improving optimization accuracy.Key parameters such as transmission ratio,gear tooth number,and modulus of planetary gear systems was used as optimization variables,and the relationship between variables was taken as constraint conditions,and performance indicators such as transmission efficiency,volume,and noise was used as optimization objectives.By constructing appropriate fitness functions,the planetary gear train design optimization problem was transformed into a multi-objective optimization problem.And experimental verification on the function test set and planetary gear design through 9 comparative algorithms was conducted.The results show that the slime mold optimization algorithm based on the weighted aggregation learning mechanism achieves significant effects in planetary gear train design optimization.Compared with traditional optimization algorithms,this algorithm can not only find the global optimal solution in a shorter time but also provide more stable and reliable optimization results.The proposed algorithm provides a novel solution for the design optimization problem of planetary gear trains,and have advantages in fast convergence speed,high optimization accuracy and good stability.6 tabs,5 figs,19 refs.
关键词
机械工程/行星轮系/黏菌优化算法/函数优化/工程优化/加权聚合学习Key words
mechanical engineering/planetary gear system/slime mould algorithm/function opti-mization/engineering optimization/weighted aggregation learning引用本文复制引用
基金项目
云南省基础研究计划项目(202101AT070082)
云南省计算机技术应用重点实验室开放基金项目()
出版年
2024
NETL
NSTL
万方数据