# Multi-Satellite Observation-Relay Transmission-Downloading Coupling Scheduling Method
## 4 Simulation Results  
Two scenarios were constructed to verify the improvement of the local search process in tabu search and the improvement of the data transmission path planning method. In the design of the simulation scenarios, the selected target points are time periods other than the orbital periods of the satellite-ground data transmission time window. In this kind of scenario, due to the limitation of storage capacity caused by the failure to download the observation data in time, the traditional scheduling methods of multi-satellite observation and single-satellite downloading will limit the number of targets observed by the satellite cluster. In contrast, by applying the multi-satellite observation-relay transmission-download coupling scheduling method, the observation data can be transmitted in time through inter-satellite data transmission, so that the satellite has more available storage capacity to observe other targets, which can maximize the observation ability and improve the observation profit of the whole satellite cluster. For the improvement of data transmission path planning, the local search method of the tabu search algorithm was kept as the conventional local search method, and we compared the improved method of transmission planning with the two conventional methods. The data transmission path planning method in the improved method is the rule-based data transmission path planning method proposed in this article. The name of the complete method is the conventional tabu search-rule-based Dijkstra data transmission path planning method (CTSRD). The data transmission path planning method used in the improved method is the strategy-based Dijkstra method [28], adapted to the problem in this article. The strategies are the min-node strategy and min-download time strategy proposed in this article. The names of the complete methods are conventional tabu search-minnode Dijkstra data transmission path planning method (CTSND) and conventional tabu search-min-download time Dijkstra data transmission path planning method (CTSTD). For the improvement of the local search process in Tabu Search, the data transmission path planning method was kept as the improved rule-based Dijkstra data transmission path planning method, and we compared the improved local search process in Tabu Search proposed in this article with the conventional Tabu Search [45]. The name of the complete method corresponding to the improved local search process is the improved tabu search-rule-based Dijkstra data transmission path planning method (ITSRD). Meanwhile, two other meta-heuristic methods were added to the simulation, including the simulated annealing algorithm [56] and the genetic algorithm [57]. The data transmission path planning method in this article was added to the simulated annealing algorithm and the genetic algorithm to solve the multi-satellite observation-relay transmission-download coupling scheduling problem. The names of these methods after adaptive transformation are SASRD and GASRD. The neighborhood construction method of SASRD adopts the neighborhood construction method in this article. In the SASRD parameters setting, the initial temperature was set to 1, the temperature decreasing rate was set to 0.95, and the ending temperature was set to 1 \(\times\) 10\({}^{-6}\). The population size of GASRD was 100, the crossover probability was 0.6, and the mutation probability was 0.4. The setting rules for parameters in the scheduling method are as follows: \(W_{warming}\) is the warning value of satellite-available electric energy, which is set based on the electric energy required for observation and downloading to accomplish a single observation mission. When the available electric energy of a satellite is less than \(W_{warming}\), the satellite cannot finish the complete observation and downloading process of a single mission. At this moment, the solution can hardly be further improved, the search process can easily fall into the local optimal state, and the search direction needs to be adjusted.