Transactions of Nanjing University of Aeronautics & Astronautics
Clustering-Scheduling Methods for Oversubscribed Short-Term Tasks of Astronomical Satellites
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Abstract:
When the observation requirement from users exceeds the satellite’s observation capability， astronomy satellite task scheduling becomes an oversubscription problem. For the oversubscribed task scheduling of astronomical satellites， a framework with a clustering phase and a short-term task scheduling phase is designed. First， a task clustering model is established to reduce the size of the oversubscribed task. Second， using the clustered results as input， we develop a mathematical model of short-term scheduling for the tasks. Finally， we propose an improved artificial bee colony algorithm with adaptive hybrid search strategies （DirectABC）. It introduces an adaptive elite global-local search strategy and an adaptive variable neighborhood optimal search strategy to the basic artificial bee colony algorithm （BasicABC）. The proposed algorithm demonstrates superior optimum-searching capability and a faster convergence speed in the simulation. In addition， it effectively reduces the number of tasks in the clustering phase and improves task completion in the short-term task scheduling phase.
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Project Supported:
This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences （No.XDA15040100）， and the Youth Innovation Promotion Association of the Chinese Academy of Sciences （No.2021146）. The authors would like to acknowledge the following people for their assistance： HU Tai， LIU Yurong， and GUO Guohang， all with the Laboratory of Satellite Operations Technology， Space Science Mission Operations Center， National Space Science Center.
Clc Number:
V474
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