[1]李崑,钱谦.基于黏菌优化算法的机械传动行星轮系多目标优化设计[J].长安大学学报(自然科学版),2024,44(4):149-160.[doi:10.19721/j.cnki.1671-8879.2024.04.014]
　LI Kun,QIAN Qian.Multi-objective optimization design of mechanical transmission planetary gear train based on slime mold optimization algorithm[J].Journal of Chang’an University (Natural Science Edition),2024,44(4):149-160.[doi:10.19721/j.cnki.1671-8879.2024.04.014]
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基于黏菌优化算法的机械传动行星轮系多目标优化设计()
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长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
第44卷
期数:
2024年4期
页码:
149-160
栏目:
机械与汽车工程
出版日期:
2024-07-10

文章信息/Info

Title:
Multi-objective optimization design of mechanical transmission planetary gear train based on slime mold optimization algorithm
文章编号:
1671-8879(2024)04-0149-12
作者:
李崑1; 2; 钱谦1; 2
(1. 昆明理工大学 信息工程与自动化学院,云南 昆明 650500; 2. 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)
Author(s):
LI Kun1; 2;  QIAN Qian1; 2
(1. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, Yunnan, China; 2. Yunnan Key Laboratory of Computer Technology Applications, Kunming University of Science and Technology, Kunming 650500, Yunnan, China)
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
Keywords:
mechanical engineering;  planetary gear system;  slime mould algorithm;  function optimization;  engineering optimization;  weighted aggregation learning
分类号:
U463.21
DOI:
10.19721/j.cnki.1671-8879.2024.04.014
文献标志码:
A
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 optimizationmodel based on improved slime mold optimization algorithm was proposed. The weighted aggregation learning mechanismwas introduced, the algorithm enables slime mold individuals to better learn andutilize excellent information from other individuals in the search space, therebyaccelerating convergence speed and improving optimization accuracy. Key parameterssuch as transmission ratio, gear tooth number, and modulus of planetary gear systemswas used as optimization variables, and the relationship between variables was taken as constraint conditions, and performance indicators such as transmissionefficiency, volume, and noise was used as optimization objectives. By constructingappropriate fitness functions, the planetary gear train design optimization problemwas transformed into a multi-objective optimization problem. And experimentalverification on the function test set and planetary gear design through 9comparative algorithms was conducted. The results show that the slime moldoptimization algorithm based on the weighted aggregation learning mechanismachieves significant effects in planetary gear train design optimization. Comparedwith traditional optimization algorithms, this algorithm can not only find the globaloptimal solution in a shorter time but also provide more stable and reliableoptimization results. The proposed algorithm provides a novel solution for the designoptimization problem of planetary gear trains, and have advantages in fastconvergence speed, high optimization accuracy and good stability.6 tabs, 5 figs, 19 refs.

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备注/Memo

备注/Memo:
收稿日期:2024-01-25
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
作者简介:李 崑(1981-),男,北京市人,工学硕士,E-mail:qianqian_yn@126.com。
通讯作者:钱 谦(1981-),男,云南安宁人,副教授,工学博士,E-mail:qianqian_yn@126.com。
更新日期/Last Update: 2024-07-10