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

系统工程与电子技术 ›› 2021, Vol. 43 ›› Issue (9): 2535-2543.doi: 10.12305/j.issn.1001-506X.2021.09.21

基于改进遗传算法的多类测控资源调度方法

薛乃阳^1,², 丁丹^1,*, 王红敏¹, 刘步花²

1. 航天工程大学电子与光学工程系, 北京 101416
2. 航天工程大学研究生院, 北京 101416

收稿日期:2020-10-26 出版日期:2021-08-20 发布日期:2021-08-26
通讯作者: 丁丹
作者简介:薛乃阳(1997—), 男, 硕士研究生, 主要研究方向为航天测控|丁丹(1980—), 男, 副研究员, 博士, 主要研究方向为航天测控|王红敏(1991—), 女, 实习研究员, 硕士, 主要研究方向为航天测控|刘步花(1994—), 女, 硕士研究生, 主要研究方向为航天测控
基金资助:
军队级项目资助课题

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

Naiyang XUE^1,², Dan DING^1,*, Hongmin WANG¹, Buhua LIU²

1. Department of Electronic and Optical Engineering, Space Engineering University, Beijing 101416, China
2. Graduate School, Space Engineering University, Beijing 101416, China

Received:2020-10-26 Online:2021-08-20 Published:2021-08-26
Contact: Dan DING
Supported by:
军队级项目资助课题

摘要/Abstract

摘要：

为了研究一类利用不同测控(telemetry, track and command, TT&C)网联合执行TT&C任务的多类TT&C资源联合调度问题, 首先对国有和商业TT&C网的不同特点进行归纳总结。其次，对问题中的约束条件进行分析和规范化描述, 并在满足所选约束的前提下, 建立问题的数学模型。进而根据问题特点, 以遗传算法为基础, 通过设计适当的编码方法和交叉、变异算子以及约束冲突处理方法, 提出一种改进遗传算法。最后，实例仿真表明, 引入的多类TT&C资源联合调度方法较传统未引入联合调度的方法能有效提高TT&C资源利用效率, 证明了所提算法求解此类TT&C调度问题具有较好的性能。

关键词: 航天测控, 多类测控资源联合调度, 改进遗传算法, 调度收益

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

中图分类号:

薛乃阳, 丁丹, 王红敏, 刘步花. 基于改进遗传算法的多类测控资源调度方法[J]. 系统工程与电子技术, 2021, 43(9): 2535-2543.

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.

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参考文献 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.

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

[1]	郭小威, 李保刚, 滕克难. 面向多阶段任务可用度的装备群维修决策模型[J]. 系统工程与电子技术, 2016, 38(3): 582-587.
[2]	刘建平, 李晶, 张天骄. 航天测控网调度的混合构造启发式算法[J]. 系统工程与电子技术, 2015, 37(7): 1569-1574.
[3]	许双伟，武小悦. 航天测控任务可靠性的混合分析方法[J]. Journal of Systems Engineering and Electronics, 2013, 35(3): 667-671.
[4]	闫华, 武小悦. 航天测控通信系统可靠性分析的改进Krylov投影算法[J]. Journal of Systems Engineering and Electronics, 2012, 34(10): 2180-2186.
[5]	陈峰, 武小悦. 基于协同进化的航天测控资源优化调度[J]. Journal of Systems Engineering and Electronics, 2009, 31(11): 2672-2676 .

基于改进遗传算法的多类测控资源调度方法

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

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