TY - GEN
T1 - Mutation particle swarm optimization for earth observation satellite mission planning
AU - Liu, Xiao Li
AU - Jiang, Wei
AU - Li, Yi Jun
PY - 2012
Y1 - 2012
N2 - Earth observation satellite mission planning is the core issue of multisatellite and multitask to coordinate control and scheduling problem. In this paper, the 0-1 integer programming model for satellite mission planning problem was constructed. We discussed the discrete particle swarm optimization (DPSO), designed decimal encoding operator of DPSO and decoding method based on the utilization of satellite resources, proposed DPSO with mutation operator (MDPSO). This algorithm not only optimizes effectively, but also has overcome the premature convergence of the particle swarm algorithm. The MDPSO can resolve the satellite mission planning effectively. Finally, we designed two sets of experiments. The first one analyzed the algorithm parameters' influence on the optimization results. Then comparing with the genetic algorithm, we verified the effectiveness of the MDPSO, and confirmed that the optimization results had been significantly improved for at least 7.8%.
AB - Earth observation satellite mission planning is the core issue of multisatellite and multitask to coordinate control and scheduling problem. In this paper, the 0-1 integer programming model for satellite mission planning problem was constructed. We discussed the discrete particle swarm optimization (DPSO), designed decimal encoding operator of DPSO and decoding method based on the utilization of satellite resources, proposed DPSO with mutation operator (MDPSO). This algorithm not only optimizes effectively, but also has overcome the premature convergence of the particle swarm algorithm. The MDPSO can resolve the satellite mission planning effectively. Finally, we designed two sets of experiments. The first one analyzed the algorithm parameters' influence on the optimization results. Then comparing with the genetic algorithm, we verified the effectiveness of the MDPSO, and confirmed that the optimization results had been significantly improved for at least 7.8%.
KW - discrete particle swarm optimization
KW - earth observation satellite
KW - mission planning
KW - mutation operator
KW - resource utilization
UR - https://www.scopus.com/pages/publications/84874361549
U2 - 10.1109/ICMSE.2012.6414189
DO - 10.1109/ICMSE.2012.6414189
M3 - 会议稿件
AN - SCOPUS:84874361549
SN - 9781467330145
T3 - International Conference on Management Science and Engineering - Annual Conference Proceedings
SP - 236
EP - 243
BT - 2012 International Conference on Management Science and Engineering, ICMSE 2012 - 19th Annual Conference Proceedings
T2 - 2012 19th Annual International Conference on Management Science and Engineering, ICMSE 2012
Y2 - 20 September 2012 through 22 September 2012
ER -