信息与控制2024，Vol.53Issue(6)：793-803,11.DOI:10.13976/j.cnki.xk.2024.3296
基于改进灰狼算法的润叶机控制参数优化
Optimization of Control Parameters of Leaf-wetting Machine Based on Improved Gray Wolf Algorithm
摘要
Abstract
We address the challenge of adapting to external factors and fluctuations in incoming material characteristics in leaf-wetting machines,which traditionally rely on manual experience to regulate control parameters.Our approach predicts tobacco quality prediction and adaptively optimizes process parameters.First,we identify key control parameters and factors affecting their regulation by systematically analyzing the process flow.We then apply a Bayesian optimization extreme gradi-ent boosting tree algorithm to model the relationship between process parameters and the moisture content and temperature of exported tobacco.Finally,using the standard quality of exported tobacco as our optimization objective,we determine the global optimal solution.To stabilize tobacco quality after wetting and improve the leaf-wetting machine's operation,we introduce an improved gray wolf algorithm with bounded stability and an adaptive penalty function.This approach accelerates convergence speed and reduces control parameter fluctuations.Experimental results show that our method reduces the moisture content and temperature fluctuation ranges of exported tobacco by 42.5%and29.9%,respectively,compared to manual adjustments,ensuring smoother operation of the leaf-wetting machine.关键词
参数优化/目标函数/极度梯度提升树/灰狼算法/润叶机Key words
parameter optimization/objective function/extreme gradient boosting tree/gray wolf algorithm/leaf-wetting machine分类
轻工纺织引用本文复制引用
张利萍,李英娜,杨苡,董俊敏,罗丹萍,许晓平..基于改进灰狼算法的润叶机控制参数优化[J].信息与控制,2024,53(6):793-803,11.基金项目
云南省基础研究专项项目(202201AS070029) （202201AS070029）
