Learning-Aided Evolutionary Algorithm for Solving Energy-Minimized Deadline-Constrained Task Scheduling Problem in Human-Cyber-Physical Systems

This work addresses an energy-minimized deadline-constrained task scheduling problem in human-cyber-physical systems. It consists of three subproblems: processor allocation, task sequencing, and processor frequency scaling. A Learning-aided Evolutionary Algorithm (LEA) is proposed to efficiently fin...

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Vydané v:	IEEE transactions on automation science and engineering Ročník 22; s. 22729 - 22741
Hlavní autori:	Cao, ZhengCai, Zhou, HaoRan, Lin, ChengRan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 2025
Predmet:	autoencoder Autoencoders evolutionary algorithm Evolutionary computation human-cyber-physical systems Job shop scheduling Machine learning Optimization Processor scheduling Radio spectrum management Resource management Schedules Sequential analysis Task scheduling
ISSN:	1545-5955, 1558-3783
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