A POA-QPSO Hybrid Algorithm for Multi-Objective Optimization of Dual-Layer Walker Constellations.

Area of Science:

• Satellite Systems Engineering
• Optimization Algorithms
• Navigation Systems

Background:

• Low Earth Orbit (LEO) satellite constellations are rapidly developing for navigation augmentation.
• Optimizing coverage and minimizing complexity in LEO constellation design presents significant challenges.

Purpose of the Study:

• To propose a hybrid optimization algorithm for multi-objective design of dual-layer Walker constellations.
• To enhance coverage performance and system efficiency in LEO navigation augmentation systems.

Main Methods:

• A hybrid optimization algorithm combining Pelican Optimization Algorithm (POA) and Quantum Particle Swarm Optimization (QPSO) was developed.
• The algorithm integrates global search (POA) and local exploitation (QPSO) with a probability-driven dual-phase search, adaptive parameter adjustment, and Pareto frontier maintenance.
• Quantum tunneling and probability factors facilitate deep search in complex environments.

Main Results:

• The POA-QPSO algorithm demonstrated superior performance on ZDT test functions, achieving an 18.5% improvement in IGD metrics compared to MOPOA and MOPSO.
• A dual-layer LEO constellation (800 km/144 satellites, 1426 km/56 satellites) achieved 92.7% global coverage, with an average PDOP of 1.78 and 5.8 visible satellites in polar regions.
• The proposed solution outperformed mainstream algorithms and traditional Medium Earth Orbit (MEO) systems in mid-to-high latitude regions.

Conclusions:

• The POA-QPSO algorithm offers an efficient solution for LEO navigation augmentation system design.
• The developed dual-layer constellation configuration provides significant improvements in coverage and performance, particularly in polar and mid-to-high latitude regions.
• This research contributes to the advancement of robust and efficient satellite-based navigation systems.