NDLI: Research on parameter optimisation of dynamic priority scheduling algorithm based on improved reinforcement learning

Content Provider	IET Digital Library
Author	Meng, Shanshan Zhu, Qiang Xia, Fei Lu, Jianfeng
Abstract	The dynamic priority scheduling algorithm is one of the real-time scheduling algorithms in a power system. However, it ignores the impact of the weight of each index when selecting indicators that affect scheduling performance. There is no definite objective function relation between weight parameters and scheduling performance. Hence, the heuristic algorithm is difficult to optimise the weight parameters. Aiming to solve this problem, a dynamic priority scheduling algorithm based on improved reinforcement learning (RL) is proposed for parameter optimisation. By learning from each other, the weighting parameters and the deadline miss rate (DMR), the global optimisation of weighting parameters can be achieved, but the learning efficiency of the conventional RL method is low. According to the task scheduling performance (the DMR) and the task characteristics, this study improves the RL action step and reward function, which accelerates the online learning speed and improves the optimisation ability of the RL algorithm. Experimental results show that the improved RL algorithm not only optimises the weight parameters but reduces the DMR, which reduces the number of iterations of RL. A scheduling algorithm optimised by RL can be better applied to industrial control and power system resource scheduling, which not only improves control efficiency but reduces scheduling costs.
Starting Page	3171
Ending Page	3178
Page Count	8
ISSN	17518687
Volume Number	14
e-ISSN	17518695
Issue Number	Issue 16, Aug (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/14/16
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2019.1468
Journal	IET Generation, Transmission & Distribution
Publisher Date	2020-05-14
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Dynamic Priority Scheduling Algorithm Heuristic Algorithm Improved Reinforcement Learning Improved RL Algorithm Knowledge Engineering Technique Learning in AI Optimisation Optimisation Technique Parameter Optimisation Power Engineering Computing Power Generation Scheduling Power System Power System Analysis Computing Power System Resource Scheduling Real-time Scheduling Algorithm Scheduling Cost Task Scheduling Performance Weight Parameter Weighting Parameter
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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