The optimization of low Earth orbit satellite constellation visibility with genetic algorithm for improved navigation potential.

To tackle visibility optimization challenges in Low Earth Orbit satellite constellations, this study proposed an enhanced framework based on an adaptive parallel Genetic Algorithm (GA). The aim was to improve both navigation accuracy and dynamic robustness. A hybrid constellation was designed by integrating polar, Walker, and Orthogonal Circular Orbits, with a dynamic relaxation factor, γ, explicitly included to compensate for J2 perturbation effects. The framework introduced three key innovations: (1) an adaptive parameter adjustment mechanism guided by population diversity entropy, (2) a parallel fitness evaluation strategy optimized for multi-core architectures, and (3) a simplified yet structured fitness function design. Experimental results showed that in a standard 100-satellite scenario, the optimized constellation achieved an average of 14.3 visible satellites-3.6% better than D-NSDE. The Position Dilution of Precision (PDOP) was reduced to 2.3, and global coverage reached 95.6%. After a single satellite failure, coverage dropped by only 3.5%, and remained at 94.8% after 10 years of orbital perturbation. Even in ultra-large-scale scenarios with 500 and 1,000 satellites, the framework maintained PDOP values of ≤ 2.8, with convergence times under 210 s. Overall, the proposed GA outperformed Particle Swarm Optimization and Dynamic Non-dominated Sorting Differential Evolution in visibility, robustness, and computational efficiency. This provides a crucial technical foundation for next-generation Global Navigation Satellite System enhancements.