Abstract

Using low-Earth orbit (LEO) mobile communication constellations to transmit remote sensing satellite data represents an emerging paradigm for overcoming the bottleneck in downloading massive amounts of Earth observation data. However, dense concurrent access across multiple satellites triggers intense resource competition, severe visible-window fragmentation, and strict resource-exclusivity constraints. To address the complex scheduling challenges caused by high laser link establishment overhead and the high-dynamic motion between remote sensing satellites and LEO communication nodes, this paper proposes an Adaptive Multi-Objective Reconstruction Evolutionary Algorithm (AMOREA). The algorithm incorporates a hybrid initialization strategy to improve the quality of the initial solution set and designs a mission-level topology reconstruction mechanism that uses four complementary decomposition operators and a multi-strategy reconstruction pool to achieve effective resource aggregation. Furthermore, an adaptive weight feedback mechanism is introduced to dynamically adjust search priorities and balance global exploration with local exploitation. Simulation results show that, under the simulation settings of this study, AMOREA reaches a 100.0% completion rate for urgent high-priority tasks and an overall average task completion rate of 89.2%. In terms of multi-objective optimization performance, AMOREA obtains the highest mean hypervolume (HV) value among the compared algorithms, improving the mean HV by approximately 19.1% over NSGA-II, 17.6% over MOEA/D, and 67.6% over the Greedy baseline. These results indicate that AMOREA can generate higher-quality Pareto solution sets and improve the efficiency of high-dynamic inter-satellite transmission scheduling under the tested simulation settings.

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