The increasing number of satellites for specific space tasks makes it difficult for traditional satellite task planning that relies on ground station planning and on-board execution to fully exploit the overall effectiveness of satellites. Meanwhile, the complex and changeable environment in space also poses challenges to the management of multi-satellite systems (MSS). To address the above issues, this paper formulates a mixed integer optimization problem to solve the autonomous task planning for MSS. First, we constructed a multi-agent-based on-board autonomous management and multi-satellite collaboration architecture. Based on this architecture, we propose a hybrid genetic algorithm with simulated annealing (H-GASA) to solve the multi-satellite cooperative autonomous task planning (MSCATP). With the H-GASA, a heuristic task scheduling scheme was developed to deal with possible task conflicts in MSCATP. Finally, a simulation scenario was established to validate our proposed H-GASA, which exhibits a superior performance in terms of computational power and success rate compared to existing algorithms.

With satellite systems rapidly developing in multiple satellites, multiple tasks, and high-speed response speed requirements, existing computing techniques face the following challenges: insufficient computing power, limited computing resources, and weaker coordination ability. Meanwhile, most methods have more significant response speed and resource utilization limitations. To solve the above problem, we propose a distributed collaborative computing framework with a genetic algorithm-based task scheduling model (DCCF-GA), which can realize the collaborative computing between multiple satellites through genetic algorithm. Specifically, it contains two aspects of work. First, a distributed architecture of satellites is constructed where the main satellite is responsible for distribution and scheduling, and the computing satellite is accountable for completing the task. Then, we presented a genetic algorithm-based task scheduling model that enables multiple satellites to collaborate for completing the tasks. Experiments show that the proposed algorithm has apparent advantages in completion time and outperforms other algorithms in resource efficiency.

To overcome the low timeliness of resource scheduling problems in spatial information networks, we propose a method based on a dynamic reconstruction of resource request queues and the autonomous coordinated scheduling of resources. First, we construct a small satellite network and combine the graph maximum flow theory to solve the link resource planning problem during inter-satellite data transmission. In addition, we design a multi-satellite resource scheduling algorithm with minimal time consumption based on graph theory. The algorithm is based on graph theory to reallocate the resource request queue to satellites with idle processing resources. Finally, we simulate the efficient resource scheduling capability in the spatial information network and empirically compare our approaches against two representative swarm intelligence baseline approaches and show that our approach has significant advantages in terms of performance and time consumption during resource scheduling.

Network flow task management involves the efficient allocation and scheduling of data flow tasks within dynamic satellite networks, aiming to effectively address frequent changes in network topology and dynamic traffic fluctuations. Existing research primarily emphasizes traffic prediction and scheduling using spatiotemporal models and machine learning. However, these approaches often depend on extensive historical data for training, making real-time adaptation to rapidly changing network topologies and traffic patterns challenging in dynamic satellite environments. Additionally, their high computational complexity and slow convergence rates hinder their efficiency in large-scale networks. To address these issues, this paper proposes a collaborative optimization framework based on Coding Multi-Path Theory (CMPT). The framework utilizes a Nash bargaining game model to simulate resource competition among the different participants, ensuring fair resource distribution and load balancing. It also integrates real-time network state monitoring with optimization algorithms, within a multi-path scheduling strategy, enabling the dynamic selection of optimal transmission paths to accommodate frequent network topology changes and traffic variations. Experimental results indicate that the proposed method reduced resource allocation task execution time by at least 18.03% compared to traditional methods and enhanced task scheduling efficiency by at least 14.01%. Although CMPT exhibited a slightly higher task latency on certain small-scale datasets compared to some baseline algorithms, its performance remains exceptional in large-scale and high-dimensional scenarios.