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Journal of Systems Engineering and Electronics ›› 2021, Vol. 32 ›› Issue (6): 1527-1538.doi: 10.23919/JSEE.2021.000128

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  • 收稿日期:2020-09-25 出版日期:2022-01-05 发布日期:2022-01-05

Optimal reconfiguration of constellation using adaptive innovation driven multiobjective evolutionary algorithm

Jiaxin HU(), Leping YANG*(), Huan HUANG(), Yanwei ZHU()   

  1. 1 College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
  • Received:2020-09-25 Online:2022-01-05 Published:2022-01-05
  • Contact: Leping YANG E-mail:joehu1989@163.com;ylp_1964@163.com;marshal-huanghuan@163.com;zywnudt@163.com
  • About author:|HU Jiaxin was born in 1989. He received his M.S. degree from National University of Defense Technology (NUDT), Changsha, China, in 2011. He is a doctoral candidate with the College of Aeronautics and Astronautics, NUDT. His research interests include astronautic mission planning and design, and multi-objective optimization. E-mail: joehu1989@163.com||YANG Leping was born in 1964. He received his B.S. and M.S. degrees from National University of Defense Technology (NUDT), Changsha, China, in 1984 and 1987, respectively. He is a professor with the College of Aeronautics and Astronautics, NUDT. His research interests include aerospace dynamics, guidance and control, and astronautic mission planning and design. E-mail: ylp_1964@163.com||HUANG Huan was born in 1985. He received his B.S., M.S., and Ph.D. degrees from National University of Defense Technology (NUDT), Changsha, China, in 2007, 2009, and 2014 respectively. He is an instructor with the College of Aeronautics and Astronautics, NUDT. His research interests include aerospace dynamics, guidance and control, and astronautic mission planning and design. E-mail: marshal-huanghuan@163.com||ZHU Yanwei was born in 1981. He received his B.S., M.S., and Ph.D. degrees from National University of Defense Technology (NUDT), Changsha, China, in 2002, 2004 and 2009, respectively. He is an associate professor with the College of Aeronautics and Astronautics, NUDT. His research interests include aerospace dynamics, guidance and control, and astronautic mission planning and design. E-mail: zywnudt@163.com
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (11802333) and the Scientific Research Program of the National University of Defence Technology (ZK18-03-34)

Abstract:

Constellation reconfiguration is a critical issue to recover from the satellite failure, maintain the regular operation, and enhance the overall performance. The constellation reconfiguration problem faces the difficulties of high dimensionality of design variables and extremely large decision space due to the great and continuously growing constellation size. To solve such real-world problems that can be hardly solved by traditional algorithms, the evolutionary operators should be promoted with available domain knowledge to guide the algorithm to explore the promising regions of the trade space. An adaptive innovation-driven multi-objective evolutionary algorithm (MOEA-AI) employing automated innovation (AI) and adaptive operator selection (AOS) is proposed to extract and apply domain knowledge. The available knowledge is extracted from the final or intermediate solution sets and integrated into an operator by the automated innovation mechanism. To prevent the overuse of knowledge-dependent operators, AOS provides top-level management between the knowledge-dependent operators and conventional evolutionary operators. It evaluates and selects operators according to their actual performance, which helps to identify useful operators from the candidate set. The efficacy of the MOEA-AI framework is demonstrated by the simulation of emergency missions. It was verified that the proposed algorithm can discover a non-dominant solution set with better quality, more homogeneous distribution, and better adaptation to practical situations.

Key words: constellation reconfiguration, emergency observations, rapid response, multi-objective optimization