Abstract
This paper analyzes the situation of multi-satellite monitor scheduling problem, according to the constraints in which, two models were established respectively, one is of total successful scheduling tasks in same weight, the other one is in different weights. Then a codec-based differential evolution algorithm was designed to solve the scheduling problem. Firstly, each evolution individual was encoded into real-coding for the use of mutation and crossover, and the optimum individual was singled out for the next iteration of the loop to get the best result by greedy selection method. After the process of the algorithm, the sequences of tasks which to assign ground station resources and the executing time were listed by decoding result code. Finally we get the results of general task scheduling and weighted task scheduling with the scheduling model. The simulating shows that the algorithm could achieve satisfactory scheduling results in satellite monitoring scheduling problem.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Schalck S (1993) Automating satellite range scheduling. Master thesis of Air Force Institute of Technology, Wright-Patterson AFB, OH
Jin G, Wu XY, Gao WB (2004) Simulation-based study on resource deployment of satellite ground station. J Syst Simul 16(11):2401–2403
Chen F, Wu XY (2010) Two-stage successive genetic algorithm for space and ground TT&C scheduling. J Nat Univ Defense Technol 32(2):17–22
Jin G (2007) CSP model for satellite and ground station TT&C resource scheduling problems. Syst Eng Electron 7:023
Lin XD, Wu XY, Liu B (2012) Study on the CSP model of satellite TT&C resource scheduling. Syst Eng Electron 34(11):2275–2279
Barbulescu L, Watson JP, Whitley LD et al (2004) Scheduling space-ground communications for the air force satellite control network. J Sched 7(1):7–34
Barbulescu L, Howe AE, Watson JP et al (2002) Satellite range scheduling: a comparison of genetic, heuristic and local search. In: Parallel problem solving from nature-PPSN VII. Springer, Berlin, pp 611–620
Barbulescu L, Howe AE, Whitley D (2006) AFSCN scheduling: how the problem and solution have evolved. Math Comput Model 43(9):1023–1037
Li YQ, Wang RX, Xu MQ et al (2012) An improved genetic algorithm for a class of multi-resource range scheduling problem. J Astronaut 33(1):85–90
Kennedy J, Eberhart R (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Networks 4(2):1942–1948
Chang F, Wu XY (2009) Satellite data transmission task scheduling based on advanced particle swarm optimization. Syst Eng Electron 31(10):2404–2408
Feng JZ, Chen X, Zheng SL (2014) Improved MOPSO algorithm and its application. Appl Res Comput 31(3):675–678
Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359
Li YF, Wu XY (2008) Application of genetic algorithm in satellite data transmission scheduling problem. Syst Eng Theory Pract 28(1):124–131
Gooley TD (1993) Automating the satellite range scheduling process. Air Force Institute of Technology, Wright-Patterson AFB
Parish SA (1994) A genetic algorithm approach to automating satellite range scheduling. Air Force Institute of Technology, Wright-Patterson AFB
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, J., Wang, S., Li, Y., Dou, C., Hu, J. (2015). Application of Differential Evolution Algorithm in Multi-satellite Monitoring Scheduling. In: Shen, R., Qian, W. (eds) Proceedings of the 27th Conference of Spacecraft TT&C Technology in China. Lecture Notes in Electrical Engineering, vol 323. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44687-4_32
Download citation
DOI: https://doi.org/10.1007/978-3-662-44687-4_32
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44686-7
Online ISBN: 978-3-662-44687-4
eBook Packages: EngineeringEngineering (R0)