基于改进遗传算法对小卫星星群任务规划研究

韩传奇; 刘玉荣; 李虎

doi:10.11728/cjss2019.01.129

基于改进遗传算法对小卫星星群任务规划研究

doi: 10.11728/cjss2019.01.129

1. 中国科学院国家空间科学中心北京 100190;
2. 中国科学院大学北京 100049

详细信息

作者简介:
韩传奇,nwpuhan@qq.com

中图分类号: V4
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- 被引次数: 0
出版历程
- 收稿日期: 2018-01-02
- 修回日期: 2018-08-28
- 刊出日期: 2019-01-15

Mission Planning for Small Satellite Constellations Based on Improved Genetic Algorithm

1 National Space Science Center, Chinese Academy of Sciences, Beijing 100190;
2 University of Chinese Academy of Sciences, Beijing 100049

摘要

摘要: 针对小卫星星群任务运行特点，建立小卫星星群多任务规划问题模型，提出了基于成像任务时间及任务均衡度的多指标优化函数.针对所建模型，采用改进型遗传算法，引入资源随机分配的解码策略及精英保留策略，保证了算法的全局收敛性，提高了算法的性能.通过仿真算例，验证了算法在解决小卫星星群多目标任务规划问题上的有效性.
- 任务规划 /
- 小卫星星群 /
- 改进遗传算法
Abstract: Based on operational features of small satellite constellation, a multi-task mission planning problem model for small satellite constellation is established, and a multi-index optimization function based on imaging task period and equilibration is proposed. Furthermore, an improved genetic algorithm involving strategies of random allocation of resources as well as elite population reservation is applied to the established model. The algorithm improves the efficiency of related solution and guarantees the convergence of the final result. The simulation result shows that the improved genetic algorithm is applicable and effective to the specific needs of mission planning problem for small satellite constellation.
- Mission planning /
- Small satellite constellation /
- Improved genetic algorithm

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参考文献(14)

[1]	LI Ming. Perspective on development of micro-small satellites[J]. Spacec. Eng., 2016, 25(6):1-5(李明. 微小卫星发展的若干思考[J]. 航天器工程, 2016, 25(6):1-5)
[2]	ZHAO Ping, CHEN Zhiming. An adapted genetic algorithm applied to satellite autonomous task scheduling[J]. Chin. Space Sci. Technol., 2016, 36(6):47-54(赵萍, 陈志明. 应用于卫星自主任务调度的改进遗传算法[J]. 中国空间科学技术, 2016, 36(6):47-54)
[3]	WU Haiyan, MENG Xin, ZHANG Yuzhu, et al. Research on the planning method for astronomy observation mission[J]. Chin. J. Space Sci., 2013, 33(5):561-568(吴海燕, 孟新, 张玉珠, 等. 面向天文观测的空间科学卫星任务规划方法研究[J]. 空间科学学报, 2013, 33(5):561-568)
[4]	HUANG Yue, QU Jinlu, JIA Shumei, et al. Long-term planning algorithm for the HXMT mission[J]. Chin. J. Space Sci., 2017, 37(6):766-772(黄跃, 屈进禄, 贾淑梅,等. HXMT卫星长期任务规划算法[J]. 空间科学学报, 2017, 37(6):766-772)
[5]	LIU Wen, LI Ligang. Mission planning of space astronomical satellite based on improved genetic algorithm[J]. Comp. Sim., 2014, 31(12):54-58(刘雯, 李立钢. 基于改进遗传算法的天文卫星任务规划研究[J]. 计算机仿真, 2014, 31(12):54-58)
[6]	WANG Huilin, HUANG Xiaojun, MA Manhao, et al. Mission scheduling technique for electronic reconnaissance satellites[J]. Sys. Eng. Elec., 2010, 32(8):1695-1699(王慧林, 黄小军, 马满好, 等. 电子侦察卫星任务调度方法[J]. 系统工程与电子技术, 2010, 32(8):1695-1699)
[7]	CHEN Yuning, XING Lining, CHEN Yingwu. Scheduling of agile satellites based on ant colony algorithm[J]. Sci. Technol. Eng., 2011, 11(3):484-489, 502(陈宇宁, 邢立宁, 陈英武. 基于蚁群算法的灵巧卫星调度[J]. 科学技术与工程, 2011, 11(3):484-489, 502)
[8]	HAO Huicheng, JIANG Wei, LI Yijun. Mission planning for agile earth observation satellites based on hybrid genetic algorithm[J]. Sci. Technol. Eng., 2013, 13(17):4972-4978(郝会成, 姜维, 李一军. 基于混合遗传算法的敏捷卫星任务规划求解[J]. 科学技术与工程, 2013, 13(17):4972-4978)
[9]	TANGPATTANAKUL P, JOZEFOWIEZ N, LOPEZ P. Multi-objective optimization for selecting and scheduling observations by agile earth observing satellites//International Conference on Parallel Problem Solving from Nature[R]. Berlin:Springer, 2012:112-121. DOI: 10.1007/978-3-642-32964-7_12
[10]	TANGPATTANAKUL P, JOZEFOWIEZ N, LOPEZ P. A multi-objective local search heuristic for scheduling Earth observations taken by an agile satellite[J]. Eur. J. Oper. Res., 2015, 245(2):542-554
[11]	HE Renjie, GAO Peng, BAI Baocun, et al. Models, algorithms and applications to the mission planning system of imaging satellites[J]. System Eng. Theor. Prac., 2011, 31(3):411-422(贺仁杰, 高鹏, 白保存, 等. 成像卫星任务规划模型、算法及其应用[J]. 系统工程理论与实践, 2011, 31(3):411-422)
[12]	JIANG Wei, PANG Xiuli. The group scheduling method for the tasks of imaging satellite network[J]. Sys. Eng. Theo. Prac., 2014, 34(8):2154-2162(姜维, 庞秀丽. 面向成像卫星组网的群任务规划方法研究[J]. 系统工程理论与实践, 2014, 34(8):2154-2162)
[13]	HOLLAND J H. Adaptation in Natural and Artificial Systems:An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence[M]. Cambridge:MIT Press, 1992
[14]	YU Hui. The improvement of genetic algorithm and its application on knapsack problem[D]. Ji'nan:Shandong normal university, 2019(于惠. 遗传算法的改进研究及在背包问题中的应用[D]. 济南:山东师范大学, 2009)