High-Efficiency Design of Mega-Constellation Based on Genetic Algorithm Coverage Optimization

The design of mega-constellations poses a formidable challenge, as the selection of an optimal configuration directly governs system-level performance, while the computational efficiency of the design methodology remains a critical concern. To address this, this paper presents a high-efficiency, versatile optimization framework predicated on a genetic algorithm. The framework is architected to design diverse configurations, including Walker-δ and Rose constellations, and supports two distinct optimization objectives: the minimization of satellite count for prescribed performance requirements, or the maximization of coverage performance for a fixed number of satellites. To ensure computational tractability, the GA is holistically integrated with a rapid and accurate coverage analysis engine based on an area-adaptive uniform point distribution. The framework’s efficacy and validity are rigorously demonstrated through extensive simulations. The results exhibit strong consistency with the industry-standard Systems Tool Kit 11 software, with average deviations for key performance indicators—namely, coverage time ratio, average coverage multiplicity, and revisit time—controlled within 1%, 0.1, and 35 s, respectively. Moreover, when applied to a specific optimization task, the algorithm successfully identified a 181-satellite constellation that satisfied a given revisit requirement. The proposed method therefore constitutes an efficient, reliable, and automated tool for the design of complex mega-constellation architectures, promoting the diversified development of constellation configurations and enhancing the performance and resource optimization of satellite systems.