A modified genetic algorithm for large-scale and joint satellite mission planning

In the context of global space technology’s rapid advancements, an increasing number of Earth observation satellites are being deployed to perform remote sensing missions, including target identification and regional surveillance. However, the inherent limitations of individual satellite systems — such as restricted observational coverage, temporal constraints, and resource capacities — necessitate collaborative multi-constellation operations to fulfill complex mission demands. This integration introduces a large-scale, multi-dimensional optimization challenge characterized by conflicting objectives (e.g., maximizing mission success rates and observational utility) and intricate constraints (e.g., satellite payload limitations and task-specific requirements). To address these complexities, we propose an enhanced hybrid genetic algorithm (GA) framework that integrates three complementary strategies: (1) an adaptive parameter tuning mechanism to balance exploration–exploitation trade-offs during evolution dynamically, (2) a tabu search-based local optimization module to refine solution quality while avoiding premature convergence, and (3) an elitist preservation protocol to retain high-performance candidates across generations. Simulation experiments conducted on representative mission scenarios demonstrate that the proposed methodology achieves superior performance compared to conventional algorithms, particularly in scenarios requiring stringent resource allocation and real-time responsiveness. The results validate the ability of the framework to solve large-scale satellite mission planning problems within relevant constraints effectively.