A knowledge-based evolutionary algorithm for relay satellite system mission scheduling problem. (December 2020)

• Record Type: Journal Article
• Authors: Song, Yanjie; Xing, Lining; Wang, Mengyuan; Yi, Yujiang; Xiang, Wen; Zhang, Zhongshan
• Is Part Of: Computers & industrial engineering. Volume 150(2020)
• Publication Date: 2020-12
• Subjects: Relay satellite system -- Scheduling -- Knowledge-based -- Evolutionary algorithm -- Genetic algorithm; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2020.106830
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: We consider relay satellite system mission scheduling (RSSMS) problem in a graph structure and construct a mathematical model. A knowledge-based algorithm including several learning techniques is proposed, named knowledge-based genetic algorithm (KBGA). A single individual optimization strategy is used in the algorithm framework. Abstract: In order to improve the operating efficiency of original satellite system, relay satellite system is used to complete the transmission of commands and data between the ground and the mission satellite. A large number of relay tasks put pressure on relay satellite system. It is necessary to generate a reasonable and effective task execution plan to improve the overall operating efficiency of system. A mix-integer mathematical model based on graph structure of relay satellite system scheduling problem was proposed. Knowledge about satellite scheduling was introduced into an evolutionary algorithm, named knowledge-based genetic algorithm (KBGA). Proposed algorithm uses knowledge to guide the process of population initialization and optimization process. Instructive knowledge makes optimization process more reliable. Through experimental verification, KBGA can effectively improve the operating efficiency of relay satellite system.