# An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks
## 2 Related Work
### Resource Scheduling Method Based on an Intelligent Optimization Algorithm  
Researchers have studied the dynamic network characteristics based on game theory models to maximize the benefits of spatial information networks [2; 3]. Dynamic graphs can represent the dynamic change characteristics of these networks. Therefore, researchers propose resource scheduling based on graph theory and design corresponding algorithms to solve emotional resource scheduling problems [26]. The cooperative scheduling problem of spatial information network resources has achieved good results. Paper [27] proposed an on-demand learning resource reservation and scheduling algorithm (RODS). In RODS, resource requests are provided to the server through the terrestrial Internet. An improved maximum queue length algorithm is used to schedule reservation requests to generate an optimized scheduling plan. Paper [28] introduced an operational planning and scheduling strategy based on the time sequence availability of consumable and replenishable resources based on the analysis of satellite function and payload resource constraints, dividing the planning and scheduling cycle into several parts and combining them. Modeling optimization, a genetic particle swarm optimization algorithm (GPSO), is proposed. Researchers [29; 30; 31; 32] proposed an ant colony optimization algorithm (ACO, ant colony optimization). Based on the observation that the problem's solution space is sparse, the ant colony algorithm is combined with the pheromone update method based on the guided solution. The basic idea of this method is that once the algorithm stops, the distribution of the pheromone trajectory can be changed by updating the pheromone trajectory with the guided solution. When the number of satellite observation task requests increases over time, the multi-satellite data downlink resource scheduling cannot be handled effectively [33]. One paper refers to a data downlink resource scheduling model that adapts to the observation task increment that is established to solve this problem. A new algorithm based on evolutionary calculation is proposed to quickly optimize the allocation and adjustment of data downlink resources to download high-priority observation data as soon as possible. Paper [34] proposes a fireworks algorithm (FWA, Fireworks algorithm), in which FWA is a swarm intelligence algorithm used to produce high-quality solutions to continuous optimization problems. FWA was first proposed in 2010 in [35] for the global optimization of complex functions. Papers [36; 37] propose several improved models based on swarm intelligence algorithms to improve the overall benefits of resource scheduling. Some researchers [38] also regard data transmission task scheduling as a combined optimization problem between satellite data transmission requirements, visible time windows, and ground station resources and propose a satellite data transmission scheduling algorithm based on the framework of differential evolution algorithm. In the process, the individual evaluation process is improved by an improved method based on 0/1 knapsack. One paper [39] formulates the data exchange between the ground station and the satellite as a multiprocessor task scheduling problem and proposes a fixed and relaxed heuristic algorithm based on the Lagrangian version to overcome the inability of standard integer programming to resolve complex technical constraints procedures. One study [40] abstracted and simplified the multi-satellite resource scheduling problem and established a mathematical model combined with the design idea of a genetic algorithm, and proposed an improved genetic algorithm for population disturbance elimination.  
However, intelligent optimization algorithms have enumeration properties, and the computation time is difficult to estimate. It is challenging to meet the stability requirements of dynamic network resource scheduling.