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Solving dynamic satellite image data downlink scheduling problem via an adaptive bi-objective optimization algorithm
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DOI：10.1016/j.cor.2023.106388.png)
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Abstract
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The satellite image data downlink scheduling problem (SIDSP) plays a critical role in the mission planning operation of earth observation satellites. However, with recent developments in satellite technology, the traditional SIDSP is poorly effective for modern satellites. To offer additional modeling flexibility and renewed capabilities, a dynamic SIDSP (DSIDSP), which combines two interlinked operations of image data segmentation and image data downlink dynamically, was introduced. We have formulated the DSIDSP as a bi-objective problem of optimizing the image data transmission rate and the service-balance degree. Harnessing the power of an adaptive large neighborhood search (ALNS) algorithm with a nondominated sorting genetic algorithm II (NSGA-II), an adaptive bi-objective memetic algorithm, NSGA2ALNS, is developed to solve DSIDSP. Results of extensive computational experiments carried out using benchmark instances are also presented. Our experimental results reveal that the NSGA2ALNS algorithm is an effective and efficient method of solving DSIDSP based on various performance metrics. In addition, new benchmark instances are also provided for DSIDSP that could be used in future research.
Keywords:
Scheduling
Satellite image data downlink scheduling
problem
Bi-objective optimization
Adaptive taboo bank
Memetic algorithm
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