# A Heterogeneous Multi-Satellite Dynamic Mission Planning Method Based on Metaheuristic Algorithms
## 1 Introduction
Minimized Handovers: Effective management of dynamic user involvement in response to fluctuating visibility between satellites and user terminals, thus reducing service interruptions and ensuring high-quality connections. By focusing on these specific results, we aim to optimize multi-satellite dynamic mission planning and enhance the performance of communication systems in varied environments. Specifically, we address the optimization of routine communication for daily users, which represents a significant gap in current research. Our dynamic task management framework improves resource utilization by adapting to diverse user needs in real time. Additionally, the framework introduces novel strategies for managing dynamic user involvement by accounting for changing visibility conditions between satellites and user terminals while minimizing handovers for users requiring continuous communication. These contributions not only address current limitations but also have practical applications for optimizing multi-satellite dynamic mission planning, ultimately enhancing communication system performance in diverse environments. Reference [17] discussed the application of heuristic algorithms for user terminal grouping in satellite communications, resulting in enhanced system performance. Study [6] further indicated that heuristic algorithms outperformed both exact and approximate methods in multi-satellite mission planning problems. Metaheuristic algorithms are improved heuristic algorithms inspired by natural phenomena and designed for general problems. Classic metaheuristic algorithms, such as the Genetic Algorithm (GA) [18], Ant Colony Optimization (ACO) [19], and Particle Swarm Optimization (PSO) [20], can achieve better solutions than heuristic algorithms through population iteration and evolution. However, while the GA is effective for exploring large search spaces, it often suffers from slow convergence, and it can become trapped in local optima due to its reliance on crossover and mutation operations. ACO, on the other hand, is inspired by the foraging behavior of ants, but its performance can degrade with increasing problem complexity, leading to longer computation times. In contrast, studies [21; 22] have shown that PSO algorithms exhibit faster convergence speeds and better performance than both the GA and ACO, particularly in dynamic environments. [PERSON] applied a discretized PSO algorithm to balance computation and communication loads in cloud computing environments. The results showed that the discretized PSO algorithm significantly improved convergence and diversity while effectively reducing load imbalance compared to other algorithms [23]. Given these insights, we chose PSO as our base algorithm and made appropriate modifications, thus proposing a method that combines Discrete Particle Swarm Optimizationwith dynamic task management. This approach aims to enhance user satisfaction and improve satellite resource management efficiency in satellite communication systems by leveraging the strengths of PSO while addressing the limitations of the GA and ACO. The discrete version of PSO allows for more precise handling of discrete decision variables, making it particularly well-suited to tasks involving user grouping and resource allocation in satellite networks. The following sections detail the content of this paper. The second part describes the simulation model structure, including GEO and MEO satellite configurations. The third part discusses the design and implementation details of the proposed Discrete PSO algorithm and the main functions and execution processes of the dynamic task management framework. The fourth part presents the simulation results by comparing the performance of the Discrete PSO algorithm with Simulated Annealing and the Genetic Algorithm, validating the advantages of our method, and analyzing the performance of dynamic task management in meeting different priority user demands. Finally, the fifth part summarizes the paper and proposes future research directions.