基于机器学习的计算机自动化任务调度算法优化研究  

Research on Optimization of Computer Automated Task Scheduling Algorithm Based on Machine Learning

作　　者：司徒莹 廖文萱 SITU Ying;LIAO Wenxuan(School of Automation,Guangdong University of Petrochemical Technology,Maoming,Guangdong 525000,China;Wuyi University,Jiangmen,Guangdong 529020,China)

机构地区：[1]广东石油化工学院自动化学院,广东茂名525000 [2]五邑大学,广东江门529020

出　　处：《移动信息》2025年第2期280-282,共3页Mobile Information

摘　　要：文中阐述了计算机任务调度的原理,包括任务调度的目标设定、约束条件分析以及传统任务调度算法的工作机制和流程.分析了机器学习在任务调度中的应用,涵盖任务特征提取、任务预测和资源分配等方面.同时,提出了基于机器学习的计算机自动化任务调度算法优化策略,包括算法选择与改进、参数优化与训练方法调整以及混合算法设计,以期为提高计算机任务调度的效率和性能提供理论支持和实践指导.This paper elaborates on the principles of computer task scheduling,including goal setting,constraint analysis,and the working mechanism and process of traditional task scheduling algorithms.Analyzed the application of machine learning in task scheduling,covering aspects such as task feature extraction,task prediction,and resource allocation.At the same time,optimization strategies for computer automation task scheduling algorithms based on machine learning were proposed,including algorithm selection and improvement,parameter optimization and training method adjustment,and hybrid algorithm design,in order to provide theoretical support and practical guidance for improving the efficiency and performance of computer task scheduling.g.

关 键 词：计算机任务调度 机器学习 算法优化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]