An Improved Local Search Algorithm with Pruning for Satellite Data Transmission Scheduling Problem

The satellite data transmission scheduling problem is, given a set of imaging satellites, ground stations, and observation targets in the scheduling scene, to study how to complete the satellite-constrained conflict adjustment and resource allocation and formulate reasonable satellite observation and receiving plans under the premise of limited satellite resource capacity and ground station resource capacity. The problem involves complex constraints, which are very difficult to solve, and it has been proven to be an NP-hard problem. The satellite data transmission scheduling optimization model was established based on studying the advantages and disadvantages of the existing satellite data transmission scheduling problem model. Combined with the characteristics of the satellite data transmission scheduling model, the method of assigning tasks to each satellite and the first-observe-then-transmit classification constraint processing model were adopted to reduce the mutual influence between the constraints and the difficulty of solving the problem. An improved local search algorithm with three pruning strategies was proposed. In order to verify the correctness and effectiveness of the model and algorithm, a data transmission scheduling scene and test example were constructed. The experimental results show that the improved local data search algorithm combined with the pruning strategy is better for soling the problem, and they provide guidance for practical engineering applications.