# A Heterogeneous Multi-Satellite Dynamic Mission Planning Method Based on Metaheuristic Algorithms
## 1 Introduction  
Nowadays, satellite communication systems play a crucial role in global communication networks. Research on aerial platforms, including satellites, is essential for enhancing network coverage and service availability in future communication networks [1]. The throughput of next-generation high-throughput satellites (HTSs) will reach hundreds of gigabits per second or even terabits per second, with the number of beams increasing from dozens to hundreds. However, with the continuous growth of user demand, efficiently managing and optimizing satellite resources has become an urgent problem to solve. In this context, reference [2] focused on joint beamforming design and optimization for RIS-aided hybrid satellite-terrestrial relay networks, where the links from the satellite and the base station (BS) to multiple users were blocked. In practical applications, Geostationary Earth Orbit (GEO) and Medium Earth Orbit (MEO) satellites communicate with vehicle-mounted, airborne, shipborne terminals and fixed stations. The coordination and scheduling of resources for multiple satellites at different orbital altitudes present significant optimization challenges. Traditional high-throughput satellites often face issues where specific areas cannot meet user demands, leading to resource waste. GEO and MEO satellites each have their advantages and disadvantages. GEO satellites, due to their geostationary orbits, have the advantages of wide coverage and fixedpositions, making them suitable for continuous coverage over large areas and for communication with fixed stations and low-mobility terminals. However, GEO satellites also suffer from significant signal transmission delays, which is particularly problematic in latency-sensitive applications. In contrast, MEO satellites, due to their lower orbital altitudes, have significantly reduced signal transmission delays, thus better meeting the needs of latency-sensitive applications. Additionally, MEO satellites have shorter orbital periods and relatively smaller coverage areas. During communication with terminals, the visibility of the communication link changes frequently due to the rapid movement of the satellites, leading to frequent handover requirements. This frequent switching not only increases system complexity but may also affect the stability and continuity of communication. In recent years, there has been a trend towards the collaborative operation of GEO and MEO satellites by leveraging their complementary advantages to enhance the overall performance and resource utilization efficiency of the communication system [3]. The collaborative operation of multi-orbit systems can improve communication quality and service coverage and offer greater flexibility in responding to sudden user demands. However, multi-orbit mission planning faces multiple challenges, including complex resource scheduling, handling of delay differences, and interference coordination. Ensuring efficient resource utilization and optimal allocation while meeting user demands remains an urgent problem to solve. For satellite communication systems, multi-satellite architecture is an extension of the large-scale MIMO (multiple-input, multiple-output) systems in single satellites, where multiple satellites work together to form a virtual MIMO system serving multiple ground terminals [4]. In flexible high-throughput satellite resource management systems, multi-satellite mission planning aims to reasonably allocate satellite resources for tasks while maximizing user satisfaction under the constraints of satellite and payload usage [5]. Traditional optimization algorithms are generally tailored for small-scale, single-satellite imaging problems, which limits their effectiveness when applied to large-scale, multi-satellite joint imaging scenarios. For instance, [PERSON] and colleagues proposed a task prioritization approach based on time windows, but their method fails to account for the complexities of dynamic user demands that arise in real-time communication scenarios [6].