Journal of System Simulation
Abstract
Abstract: With the increase of the number of satellites and targets, the space for solving satellite task planning increases rapidly. For large-scale multi-satellite and multi-task planning, a hierarchical optimization method, which consists of Critical Path Method (CPM) and Genetic Algorithm (GA) is proposed. The method decomposes the satellite task planning into two subproblems: task allocation and single-satellite task processing. Task allocation is realized by GA and a task allocation result corresponds to an individual of the population. The CPM is used in the single-satellite task processing to solve the fitness of each individual, which improves the optimization efficiency, ensures the maximum observation benefit under the current task assignment condition, and improves the global optimization ability of the algorithm. Simulation results show that for a given set of 6 examples, the mission completion rate is over 99.7%, which proves that the method has good stability and global searching ability. Compared with the existing methods, the method also enhances the optimization efficiency greatly, and the larger the mission scale is, the higher the optimization efficiency will be improved.
Recommended Citation
Mao, Liheng; Qing, Deng; Liu, Rouni; and Kong, Xianglong
(2021)
"CPM-GA for Multi-satellite and Multi-task Simulation Scheduling,"
Journal of System Simulation: Vol. 33:
Iss.
1, Article 22.
DOI: 10.16182/j.issn1004731x.joss.19-0301
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol33/iss1/22
First Page
205
Revised Date
2020-03-18
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.19-0301
Last Page
214
CLC
TP391
Recommended Citation
Mao Liheng, Deng Qing, Liu Rouni, Kong Xianglong. CPM-GA for Multi-satellite and Multi-task Simulation Scheduling[J]. Journal of System Simulation, 2021, 33(1): 205-214.
DOI
10.16182/j.issn1004731x.joss.19-0301
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