电子电气工程与控制

面向测控数传资源一体化场景的卫星地面站资源多目标优化方法

  • 孙刚 ,
  • 彭双 ,
  • 陈浩 ,
  • 伍江江 ,
  • 李军
展开
  • 国防科技大学 电子科学学院, 长沙 410073

收稿日期: 2021-07-16

  修回日期: 2021-08-19

  网络出版日期: 2021-09-22

基金资助

国家自然科学基金(61806211, U19A2058, 62106276);湖南省自然科学基金(2020JJ4103)

Multi-objective optimization method oriented to integrated scenario of TT & C resources and data transmission resources

  • SUN Gang ,
  • PENG Shuang ,
  • CHEN Hao ,
  • WU Jiangjiang ,
  • LI Jun
Expand
  • College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Received date: 2021-07-16

  Revised date: 2021-08-19

  Online published: 2021-09-22

Supported by

National Natural Science Foundation of China (61806211, U19A2058, 62106276); Natural Science Foundation of Hunan Province (2020JJ4103)

摘要

近年来, 随着电子技术的发展, 地面站测控设备与数传设备逐渐趋同, 呈现出功能一体化的特性, 充分利用该特性可有效提高地面站设备资源的利用率, 缓解星地通信中地面站资源相对匮乏的现实难题。针对问题特征和实际需求, 建立了以最小化任务冲突时长、最大化天线负载均衡度以及最大化任务集聚度为优化目标的约束满足模型, 提出了面向测控数传资源一体化场景的卫星地面站资源规划多目标优化算法KG-NSGA-II-TTC&DT。该算法针对优化目标设计了负载均衡算子、任务集聚算子以及迭代修复冲突消解算子, 并以膝点引导算法进程, 有效提升了问题求解的优化性和针对性。实验结果表明, 与NSGA-II-TTC&DT算法相比, KG-NSGA-II-TTC&DT在世代距离(GD)指标上实现了16.75%的平均性能提升, 在最小化任务冲突时长、最大化天线负载均衡度以及最大化任务集聚度3个优化目标上分别实现了6.67%、9.28%以及1.87%的平均优化性能提升, 其中负载均衡算子、任务集聚算子以及迭代修复冲突消解算子的优化性能贡献率分别为31.50%、15.60%、70.57%。

本文引用格式

孙刚 , 彭双 , 陈浩 , 伍江江 , 李军 . 面向测控数传资源一体化场景的卫星地面站资源多目标优化方法[J]. 航空学报, 2022 , 43(9) : 326114 -326114 . DOI: 10.7527/S1000-6893.2021.26114

Abstract

With the development of the electronic technology in recent years, TT&C resources and data transmission resources in satellite ground stations are gradually converging, showing the characteristic of functional integration. Making full use of this feature can effectively improve the utilization rate of satellite ground station resources and alleviate the problem of satellite ground station resource shortage in satellite-to-ground communications. In view of the characteristics of the problem and actual requirements, a constraint satisfaction model is established with the optimization objectives of minimizing task-conflict time, maximizing load-balance degree, and maximizing task-clustering degree. A satellite range scheduling method named KG-NSGA-II-TTC&DT is proposedfor the integrated scenario of TT&C resources and data transmission resources. The load-balance operator, task-clustering operator, and conflict-resolution operator based on iterative repair are designed in the algorithm. The knee point is also used to guide the process of the algorithm, which effectively improves the optimization and pertinence. Experimental results show that compared with the NSGA-II-TTC&DT, the KG-NSGA-II-TTC&DT achieves an average performance improvement of 16.75% in the Generation Distance (GD) indicator, and an improvement of 6.67%, 9.28% and 1.87% in the three optimization objectives of minimizing task conflict time, maximizing load-balance degree, and maximizing task clustering degree, respectively. The contribution rate of the load-balance operator, task clustering operator, and conflict resolution operator based on iterative repair is 31.50%, 15.60%, and 70.57%, respectively.

参考文献

[1] CHEN H. Planning and scheduling method for the earth 's surface electromagnetic environment detection satellite resources[D]. Changsha: National University of Defense Technology, 2009: 1-21 (in Chinese). 陈浩. 地表电磁环境探测卫星的资源规划调度方法[D]. 长沙: 国防科学技术大学, 2009: 1-21.
[2] VAZQUEZ R, PEREA F, GALÁN VIOQUE J. Resolution of an Antenna-Satellite assignment problem by means of integer linear programming[J]. Aerospace Science and Technology, 2014, 39: 567-574.
[3] MARINELLI F, NOCELLA S, ROSSI F, et al. A Lagrangian heuristic for satellite range scheduling with resource constraints[J]. Computers & Operations Research, 2011, 38(11): 1572-1583.
[4] BROWN N, ARGUELLO B, NOZICK L, et al. A heuristic approach to satellite range scheduling with bounds using Lagrangian relaxation[J]. IEEE Systems Journal, 2018, 12(4): 3828-3836.
[5] SPANGELO S, CUTLER J, GILSON K, et al. Optimization-based scheduling for the single-satellite, multi-ground station communication problem[J]. Computers & Operations Research, 2015, 57: 1-16.
[6] SHE Y C, LI S, ZHAO Y B. Onboard mission planning for agile satellite using modified mixed-integer linear programming[J]. Aerospace Science and Technology, 2018, 72: 204-216.
[7] ZHANG Z J, ZHANG N, FENG Z R. Multi-satellite control resource scheduling based on ant colony optimization[J]. Expert Systems With Applications, 2014, 41(6): 2816-2823.
[8] ZHANG Z J, HU F N, ZHANG N. Ant colony algorithm for satellite control resource scheduling problem[J]. Applied Intelligence, 2018, 48(10): 3295-3305.
[9] 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.
[10] CHEN H, LI J, JING N, et al. Scheduling model and algorithms for autonomous electromagnetic detection satellites[J]. Acta Aeronautica et Astronautica Sinica, 2010, 31(5): 1045-1053 (in Chinese). 陈浩, 李军, 景宁, 等. 电磁探测卫星星上自主规划模型及优化算法[J]. 航空学报, 2010, 31(5): 1045-1053.
[11] LI Y Q, WANG R X, LIU Y, et al. Satellite range scheduling with the priority constraint: An improved genetic algorithm using a station ID encoding method[J]. Chinese Journal of Aeronautics, 2015, 28(3): 789-803.
[12] SONG Y J, ZHANG Z S, SONG B Y, et al. Improved genetic algorithm with local search for satellite range scheduling system and its application in environmental monitoring[J]. Sustainable Computing: Informatics and Systems, 2019, 21: 19-27.
[13] CHEN H, ZHOU Y R, DU C, et al. A satellite cluster data transmission scheduling method based on genetic algorithm with rote learning operator[C]//2016 IEEE Congress on Evolutionary Computation. Piscataway: IEEE Press, 2016: 5076-5083.
[14] TANG Y Y, WANG Y K, CHEN J Y, et al. Uplink scheduling of navigation constellation based on immune genetic algorithm[J]. PLoS One, 2016, 11(10): e0164730.
[15] 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.
[16] VAZQUEZ A J, ERWIN R S. On the tractability of satellite range scheduling[J]. Optimization Letters, 2015, 9(2): 311-327.
[17] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.
[18] SUN G, CHEN H, PENG S, et al. Multi-objective optimization algorithm for satellite range scheduling based on preference MOEA[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 524475 (in Chinese). 孙刚, 陈浩, 彭双, 等. 一种基于偏好MOEA的卫星地面站资源多目标优化算法[J]. 航空学报, 2021, 42(4): 524475.
[19] SONG Y J, MA X, LI X J, et al. Learning-guided nondominated sorting genetic algorithm Ⅱ for multi-objective satellite range scheduling problem[J]. Swarm and Evolutionary Computation, 2019, 49: 194-205.
[20] DU Y H, XING L N, ZHANG J W, et al. MOEA based memetic algorithms for multi-objective satellite range scheduling problem[J]. Swarm and Evolutionary Computation, 2019, 50: 100576.
[21] ZHANG J W, XING L N, PENG G S, et al. A large-scale multiobjective satellite data transmission scheduling algorithm based on SVM+NSGA-Ⅱ[J]. Swarm and Evolutionary Computation, 2019, 50: 100560.
[22] WANG Z H, ZHANG Z S, CHEN Y W. Multi-objective optimization of satellite-ground time synchronization scheduling problem[C]//2019 IEEE Congress on Evolutionary Computation (CEC). Piscataway: IEEE Press, 2019: 530-537.
[23] WANG J. Research on modeling and optimization techniques in united mission scheduling of imaging satellites[D]. Changsha: National University of Defense Technology, 2007: 38-86 (in Chinese). 王钧. 成像卫星综合任务调度模型与优化方法研究[D]. 长沙: 国防科学技术大学, 2007: 38-86.
[24] LI L M, WANG Y L, TRAUTMANN H, et al. Multiobjective evolutionary algorithms based on target region preferences[J]. Swarm and Evolutionary Computation, 2018, 40: 196-215.
[25] VAN VELDHUIZEN D A, LAMONT G. Evolutionary computation and convergence to a pareto front[C]//Proceedings of the Late Breaking Papers at the Genetic Programming 1998 Conference, 1998: 221-228.
[26] DEB K, GUPTA S. Understanding knee points in bicriteria problems and their implications as preferred solution principles[J]. Engineering Optimization, 2011, 43(11): 1175-1204.
[27] BRANKE J, DEB K, DIEROLF H, et al. Finding knees in multi-objective optimization[M]//Lecture Notes in Computer Science. Berlin: Springer, 2004: 722-731.
[28] DAS I. On characterizing the "knee" of the Pareto curve based on Normal-Boundary Intersection[J]. Structural Optimization, 1999, 18(2): 107-115.
[29] RACHMAWATI L, SRINIVASAN D. A multi-objective genetic algorithm with controllable convergence on knee regions[C]//2006 IEEE International Conference on Evolutionary Computation. Piscataway: IEEE Press, 2006: 1916-1923.
[30] CHIU W Y, YEN G G, JUAN T K. Minimum Manhattan distance approach to multiple criteria decision making in multiobjective optimization problems[J]. IEEE Transactions on Evolutionary Computation, 2016, 20(6): 972-985.
[31] DAVID H. MOEAFramework[EB/OL]. (2019-12-30)[2021-05-29]. http://moeaframework.org/.
[32] Analytical Graphics Inc. Satellite Tool Kit 10.1.3[EB/OL]. [2021-05-29]. https://www.agi.com/.
文章导航

/