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Multi-objective optimization design of mechanical transmission planetary gear train based on slime mold optimization algorithm(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2024年4期
Page:
149-160
Research Field:
机械与汽车工程
Publishing date:

Info

Title:
Multi-objective optimization design of mechanical transmission planetary gear train based on slime mold optimization algorithm
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)
Keywords:
mechanical engineering;  planetary gear system;  slime mould algorithm;  function optimization;  engineering optimization;  weighted aggregation learning
PACS:
U463.21
DOI:
10.19721/j.cnki.1671-8879.2024.04.014
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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Last Update: 2024-07-10