基于改进遗传算法的多类测控资源调度方法

doi:10.12305/j.issn.1001-506X.2021.09.21

Abstract

Abstract:

In order to study the joint scheduling problem of multi class TT & C(telemetry, track and command) resources using different TT & C networks to jointly perform TT & C tasks, the different characteristics of state-owned and commercial TT & C networks are summarized. Secondly, the constraint conditions in the problem are analyzed and described, and the mathematical model of the problem is established on the premise of satisfying the selected constraints. Then, according to the characteristics of the problem, based on genetic algorithm, an improved genetic algorithm is proposed by designing appropriate coding methods, crossover and mutation operators and constraint conflict handling methods. Finally, the simulation results show that the proposed method can effectively improve the utilization efficiency of TT & C resources compared with the traditional method without joint scheduling, which proves that the proposed algorithm has better performance in solving this kind of TT & C scheduling problem.

Key words: aerospace telemetry, track and command (TT&C), joint scheduling of multiple TT&C resources scheduling, improved genetic algorithm, scheduling revenue

CLC Number:

Naiyang XUE, Dan DING, Hongmin WANG, Buhua LIU. Multi-type TT&C resource scheduling method based on improved genetic algorithm[J]. Systems Engineering and Electronics, 2021, 43(9): 2535-2543.

Figures/Tables 9

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References 31

1	张天骄, 李济生, 李晶, 等. 基于混合蚁群优化的天地一体化调度方法[J]. 系统工程与电子技术, 2016, 38 (7): 1555- 1562.
	ZHANG T J , LI J S , LI J , et al. Integrated scheduling method for heaven and earth based on hybrid ant colony optimization[J]. Systems Engineering and Electronics, 2016, 38 (7): 1555- 1562.
2	BONISSONE P P , SUBBU R , EKLUND N , et al. Evolutionary algorithms+domain knowledge=real-world evolutionary computation[J]. IEEE Trans.on Evolutionary Computation, 2006, 10 (3): 256- 280. doi: 10.1109/TEVC.2005.857695
3	STOTTLER R, RICHARDS R. Managed intelligent deconfliction and scheduling for satellite communication[C]//Proc. of the IEEE Aerospace Conference, 2018.
4	GOOLEY T D , BORSI J J , MOORE J T . Automating air force satellite control network (AFSCN) scheduling[J]. Mathematical and Computer Modelling, 1996, 24 (2): 91- 101. doi: 10.1016/0895-7177(96)00093-3
5	XHAFA F , SUN J , BAROLLI A , et al. Genetic algorithms for satellite scheduling problems[J]. Mobile Information Systems, 2012, 8 (4): 351- 377. doi: 10.1155/2012/717658
6	DAMIANI S, DREIHAHN H, NOLL J, et al. A planning and scheduling system to allocate ESA ground station network ser-vices[C]//Proc. of the the International Conference on Automated Planning and Scheduling, 2007.
7	PARISH S A. A genetic algorithm approach to automating satellite range scheduling[D]. Ohio: Air Force Institute of Technology, 1994.
8	BARBULESCU L, HOWE A E, WATSON J P, et al. Satellite range scheduling: a comparison of genetic, heuristic and local search[C]//Proc. of the International Conference on Parallel Problem Solving from Nature, 2002: 611-620.
9	BARBULESCU L , WATSON J P , WHITLEY L D , et al. Scheduling space-ground communications for the air force satellite control network[J]. Journal of Scheduling, 2004, 7 (1): 7- 34. doi: 10.1023/B:JOSH.0000013053.32600.3c
10	SARKHEYLI A , BAGHERI A , GHORBANI-VAGHEI B , et al. Using an effective tabu search in interactive resources scheduling problem for LEO satellites missions[J]. Aerospace Science and Technology, 2013, 29 (1): 287- 295. doi: 10.1016/j.ast.2013.04.001
11	TANGPATTANAKUL P , JOZEFOWIEZ N , LOPEZ P . A multi-objective local search heuristic for scheduling earth observations taken by an agile satellite[J]. European Journal of Operational Research, 2015, 245 (2): 542- 554. doi: 10.1016/j.ejor.2015.03.011
12	VAZQUEZ R , PEREA F , VIOQUE J G . Resolution of an antenna-satellite assignment problem by means of integer linear programming[J]. Aerospace Science and Technology, 2014, 39, 567- 574. doi: 10.1016/j.ast.2014.06.002
13	陈峰. 多星测控调度问题的遗传算法研究[D]. 长沙: 国防科学技术大学, 2010.
	CHEN F. Research on genetic algorithm for multi-satellite TT&C Scheduling Problem [D]. Changsha: National University of Defense Technology, 2010.
14	李玉庆, 王日新, 徐敏强, 等. 基于改进遗传算法的一类多资源测控调度问题研究[J]. 宇航学报, 2012, 33 (1): 85- 90. doi: 10.3873/j.issn.1000-1328.2012.01.012
	LI Y Q , WANG R X , XU M Q , et al. An improved genetic algorithm for a class of multi-resource range scheduling problem[J]. Journal of Astronautics, 2012, 33 (1): 85- 90. doi: 10.3873/j.issn.1000-1328.2012.01.012
15	徐爱民, 张国亭. 对我国商业航天测控管理有关问题的初步探讨[J]. 飞行器测控学报, 2017, 36 (3): 157- 163.
	XU A M , ZHANG G T . Discussions on TT&C management of commercial space in China[J]. Journal of Spacecraft TT&C Technology, 2017, 36 (3): 157- 163.
16	杨诗瑞. "编织"中国航天测控网[J]. 太空探索, 2020, (9): 22- 27. doi: 10.3969/j.issn.1009-6205.2020.09.009
	YANG S R . "Weaving" Chinese TT&C network[J]. Space exploration, 2020, (9): 22- 27. doi: 10.3969/j.issn.1009-6205.2020.09.009
17	郭夏锐. 商业卫星测控发展现状及趋势[J]. 国际太空, 2019, (10): 44- 48. doi: 10.3969/j.issn.1009-2366.2019.10.008
	GUO X R . Status and trends of commercial satellite measurement and control[J]. International Space, 2019, (10): 44- 48. doi: 10.3969/j.issn.1009-2366.2019.10.008
18	DURRESI A , DURRESI M , BAROLLI L , et al. MPLS traffic engineering for multimedia on satellite networks[J]. Journal of Mobile Multimedia, 2009, 5 (1): 3- 11.
19	XUAN K, ZHAO G, TANIAR D, et al. Continuous range search query processing in mobile navigation[C]//Proc. of the IEEE 14th International Conference on Parallel and Distributed Systems, 2008: 361-368.
20	XUAN K , ZHAO G , TANIAR D , et al. Voronoi-based multi-level range search in mobile navigation[J]. Multimedia Tools and Applications, 2011, 53 (2): 459- 479. doi: 10.1007/s11042-010-0498-y
21	魏兴. 一体化测控技术, 商业航天测控的未来[J]. 卫星与网络, 2020, (8): 42- 47.
	WEI X . Integrated TT&C technology, the future of commercial aerospace TT&C[J]. Satellite and Internet, 2020, (8): 42- 47.
22	ZHANG G H , HU Y F , SUN J H , et al. An improved genetic algorithm for the flexible job shop scheduling problem with multiple time constraints[J]. Swarm and Evolutionary Computation, 2020, 54 (4): 100664.
23	AMJAD M K , BUTT S I , KOUSAR R , et al. Recent research trends in genetic algorithm based flexible job shop scheduling problems[J]. Mathematical Problems in Engineering, 2018, (8): 1- 32.
24	LU P H , WU M C , TAN H , et al. A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems[J]. Journal of Intelligent Manufacturing, 2018, 29 (1): 19- 34.
25	BARKAOUI M , BERGER J . A new hybrid genetic algorithm for the collection scheduling problem for a satellite constellation[J]. Journal of the Operational Research Society, 2020, 71 (9): 1390- 1410. doi: 10.1080/01605682.2019.1609891
26	LIU C, CHAI R, CHEN Q B. IoT gateway association and data scheduling for delay optimization in LEO satellite systems[C]//Proc. of the IEEE/CIC International Conference on Communications in China, 2020: 993-998.
27	ZHU W M , HU X X , XIA W , et al. A two-phase genetic annealing method for integrated earth observation satellite schedu-ling problems[J]. Soft Computing, 2019, 23 (1): 181- 196. doi: 10.1007/s00500-017-2889-8
28	LEE C K H . A review of applications of genetic algorithms in operations management[J]. Engineering Applications of Artificial Intelligence, 2018, 76, 1- 12.
29	LUO Z Y, DENG M, YAO Z Q, et al. Distributed blanket jamming resource scheduling for satellite navigation based on particle swarm optimization and genetic algorithm[C]//Proc. of the IEEE 20th International Conference on Communication Technology, 2020: 611-616.
30	ZHI B E , SHI R , GAN L , et al. Multi-satellites imaging scheduling using individual reconfiguration based integer coding genetic algorithm[J]. Acta Astronautica, 2021, 178, 645- 657. doi: 10.1016/j.actaastro.2020.08.041
31	XHAFA F, HERRERO X, BAROLLI A, et al. Using STK toolkit for evaluating a GA base algorithm for ground station scheduling[C]//Proc. of the 7th International Conference on Complex, Intelligent, and Software Intensive Systems, 2013.

测控资源属性	用户卫星属性	调度收益
国有	国有	10
国有	商用	5
商用	国有	6
商用	商用	10

卫星SSC号	ST_k	卫星SSC号	ST_k	卫星SSC号	ST_k	卫星SSC号	ST_k
40 894	2	40 144	1	43 099	2	44 534	1
40 961	2	39 013	1	43 260	2	44 539	2
43 254	1	40 109	1	43 262	1	44 622	2
39 548	1	40 111	1	43 275	2	44 785	1
40 213	1	40 305	1	43 946	1	46 025	2
44 215	1	40 878	1	44 207	1	46 462	1

参数	代号	取值
染色体种群规模	NIND	40
代沟	GGAP	0.9
交叉概率	XOVR	0.8
变异概率	PMU	0.6
最大迭代次数	MAXGEN	1 000

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Multi-type TT&C resource scheduling method based on improved genetic algorithm

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