HT-NSGA-III: A Heuristic Tabu-Enhanced Approach for Multi-Day Satellite TT&C Resource Scheduling

With the rapid growth of the number of satellites in orbit, satellite Telemetry, Tracking and Command (TT&C) systems are facing unprecedented challenges. As a key bridge for communication between ground stations and satellites, the TT&C system ensures that satellites operate normally according to predefined requirements, and the scheduling of its resources is a combinatorial optimization problem involving multi-objective optimization with complex constraints. To solve this problem, we propose a Heuristic Tabu-enhanced Non-dominated Sorting Genetic Algorithm III (HT-NSGA-III), which organically integrates heuristic strategy and tabu search mechanism on the basis of the NSGA-III framework. Since current scheduling models are mostly limited to $\mathbf{2 4}$-hour cycles with significant differences to real application scenarios, we first construct a more practical multi-day scheduling model based on real constraints. Then, by introducing domain knowledge, a heuristic method is used to initialize the population of the evolutionary algorithm and implement heuristic mutations during the evolutionary process, with the aim of improving the quality of the initial population and the efficiency of the evolutionary algorithm. Finally, local optimization is performed by integrating tabu search and load balancing mechanisms to avoid the algorithm falling into local optimal solutions. Experiments were conducted using real data from commercial space TT&C scenarios. The experimental results show that, compared with traditional multi-objective optimization algorithms, HT-NSGA-III has significant advantages in key performance indicators such as task completion rate and load standard deviation, and provides an effective method for solving the increasingly complex satellite TT&C resource scheduling problem.