An improved genetic algorithm for the integrated satellite imaging and data transmission scheduling problem. (March 2022) - Zhang, Jiawei; Xing, Lining
Highlights: The integrated satellite imaging and data transmission scheduling problem is quite complex. An improved genetic algorithm is presented. The effectiveness of several operators is validated. Our approach can obtain high-quality solutions within a limited time period. Our approach performs well for the large scale optimization instances. Abstract: The study of Integrated Satellite Imaging and Data Transmission Scheduling Problem (ISIDTSP) has assumed increasing importance due to the growing number of satellites observing large quantities of targets and seeking to transmit their images to the ground stations. This paper formulates the ISIDTSP as a mixed integer programming model and develops an improved genetic algorithm. With regard to the individual representation, a novel idea of encoding and decoding is adopted to match the specific request with the corresponding satellite-ground resources, and a conception of conflicting request set is proposed to limit the chromosome length, thereby reducing the algorithmic time complexity. For the trade-off between diversity and convergence, several effective operators are introduced, including the population initialization based on the way of uniform design, the multi-point greedy mutation and the adaptive selection. Evaluations with two test cases demonstrate the efficiency of the proposed algorithm and show its ability to obtain high-quality solutions within an acceptable time period for the large scale optimization instances.