2013
DOI: 10.4028/www.scientific.net/amr.791-793.1341
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An Ant Colony Algorithm for Remote Satellite and Ground Integration Scheduling Problem in Parallel Environment

Abstract: The algorithms for solving the remote satellite scheduling problem are less effective usually in single computing environment. This paper designed a framework of ant colony algorithm for remote satellite and ground integration scheduling problem in the parallel environment, and given the detail of key steps in the algorithm. Experiments are show at the end of this paper to prove effective and validation.

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Cited by 8 publications
(2 citation statements)
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“…He et al [19] designed a combination algorithm to solve the problem of task planning for an earth observation satellite in order to avoid the instability problem associated with providing a solution using a single traditional algorithm. In order to improve the efficiency of remote sensing satellite scheduling, Gao et al [20] designed the ant colony algorithm for remote sensing satellite ground integrated task planning. Jiang and Pang [21] designed an adaptive ant colony algorithm by designing a task merging mechanism to solve the problem of huge user requests.…”
Section: Overview Of Satellite Task Planning Algorithmsmentioning
confidence: 99%
“…He et al [19] designed a combination algorithm to solve the problem of task planning for an earth observation satellite in order to avoid the instability problem associated with providing a solution using a single traditional algorithm. In order to improve the efficiency of remote sensing satellite scheduling, Gao et al [20] designed the ant colony algorithm for remote sensing satellite ground integrated task planning. Jiang and Pang [21] designed an adaptive ant colony algorithm by designing a task merging mechanism to solve the problem of huge user requests.…”
Section: Overview Of Satellite Task Planning Algorithmsmentioning
confidence: 99%
“…He et al (2011) formulated the model of satellites observation scheduling problem with task merging and developed the simulated annealing algorithm to solve the mission scheduling problem. Gao et al (2013) designed a framework of ant colony algorithm for remote satellite and ground integration scheduling problem in the parallel environment. Hao et al (2013) proposed a combination of genetic and ant colony algorithms to solve the mission scheduling problem.…”
Section: Introductionmentioning
confidence: 99%