Abstract
Multi-satellite observation scheduling is a complex combinatorial optimization problem while the optimization process is very complicated. Given this, in this paper we propose an effective hybrid optimization method based on the combination of particle swarm optimization (PSO) and genetic algorithm (GA). Using this method, it greatly improves the efficiency of the optimization. Simulation result demonstrates the effectiveness of hybrid algorithm can efficiently solve the scheduling problem of multi-satellite observations. Simulation result demonstrates that the hybrid algorithm can efficiently solve the scheduling problem of multi-satellite observations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Frank, J., Jonsson, A., Morris, R., Smith, D.: Planning and scheduling for fleets of earth observing satellites, pp. 18–22. Citeseer (2002)
Al Globus, J.C., Lohn, J., Morris, R.: Scheduling earth observing fleets using evolutionary algorithms: Problem description and approach, pp. 27–29. Citeseer (2002)
Lu, P., Xu, P.: Scheduling of Imaging Reconnaissance Satellites (IRS) Based on Greedy Algorithm. Computer Simulation 25(2), 37–40 (2008)
Wang, D., Zhu, J., Xue, B.: Mission Planning for Electornic Reconnaissance Satelites Based on Genetic Algorithm. Computer Simulation 26(8), 53–56 (2008)
Liu, X., Wu, X.: Study of GATS Algorithm for Multi-satelites TT&C Scheduling Problem. Journal of Astronautics 30(5) (2009)
Gao, H., Zhou, C., Gao, L.: General Particle Swarm Optimization Model. Chinese Journal of Computers 28(2) (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, Y., Zhang, D., Zhou, M., Zou, H. (2012). Multi-satellite Observation Scheduling Algorithm Based on Hybrid Genetic Particle Swarm Optimization. In: Zeng, D. (eds) Advances in Information Technology and Industry Applications. Lecture Notes in Electrical Engineering, vol 136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-26001-8_58
Download citation
DOI: https://doi.org/10.1007/978-3-642-26001-8_58
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-26000-1
Online ISBN: 978-3-642-26001-8
eBook Packages: EngineeringEngineering (R0)