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

## TL;DR

A new hybrid algorithm optimizes satellite constellations for better coverage and performance in challenging navigation environments.

## Contribution

A novel POA-QPSO hybrid algorithm is introduced for multi-objective optimization of dual-layer Walker satellite constellations.

## Key findings

- The hybrid POA-QPSO algorithm outperforms MOPSO and MOPOA in convergence accuracy and solution diversity on ZDT benchmarks.
- The optimized dual-layer constellation achieves 92.7% global coverage and an average PDOP of 1.78.
- The proposed solution improves IGD metrics by 18.5% and provides better performance in polar and mid-to-high latitude regions.

## Abstract

What are the main findings?
The proposed hybrid POA-QPSO algorithm, utilizing a probability-driven dual-phase search mechanism, significantly outperforms MOPSO and MOPOA in convergence accuracy and solution diversity across ZDT benchmark functions.The optimized dual-layer Walker constellation (144 satellites at 800 km/50° and 56 satellites at 1426 km/82°) achieves 92.7% global coverage and an average PDOP of 1.78, superior to traditional single-layer configurations.

The proposed hybrid POA-QPSO algorithm, utilizing a probability-driven dual-phase search mechanism, significantly outperforms MOPSO and MOPOA in convergence accuracy and solution diversity across ZDT benchmark functions.

The optimized dual-layer Walker constellation (144 satellites at 800 km/50° and 56 satellites at 1426 km/82°) achieves 92.7% global coverage and an average PDOP of 1.78, superior to traditional single-layer configurations.

What are the implications of the main findings?
The integration of diversity-triggered quantum behavior effectively overcomes premature convergence in high-dimensional discrete search spaces, providing a robust framework for solving complex aerospace engineering optimization problems.The study presents a cost-effective, high-precision architectural blueprint for next-generation LEO navigation augmentation systems, ensuring enhanced service availability in challenging environments such as urban canyons and polar regions.

The integration of diversity-triggered quantum behavior effectively overcomes premature convergence in high-dimensional discrete search spaces, providing a robust framework for solving complex aerospace engineering optimization problems.

The study presents a cost-effective, high-precision architectural blueprint for next-generation LEO navigation augmentation systems, ensuring enhanced service availability in challenging environments such as urban canyons and polar regions.

The rapid development of low earth orbit (LEO) satellite constellations for navigation augmentation represents significant challenges in optimizing coverage performance while minimizing system complexity. A hybrid optimization algorithm based on pelican optimization algorithm and quantum particle swarm optimization (POA-QPSO) is proposed in this paper for multi-objective optimization design of dual-layer Walker constellations. The algorithm integrates the global search capability of the POA and the local exploitation ability of QPSO, effectively balancing exploration and exploitation through a probability-driven dual-phase search mechanism, a three-tier adaptive parameter adjustment strategy, and a pareto frontier maintenance mechanism. Probability factor and quantum tunneling facilitate low-cost deep search in complex non-convex environments. Experiments demonstrate that the algorithm outperforms MOPOA and MOPSO on ZDT test functions, with an 18.5% improvement in IGD metrics. In LEO constellation optimization, the designed dual-layer configuration (800 km/144 satellites in the first layer and 1426 km/56 satellites in the second layer) achieves a 92.7% global coverage, with an average PDOP of 1.78 and 5.8 visible satellites in polar regions. Furthermore, comparative benchmark tests show that the proposed solution outperforms most mainstream algorithms and performs better than traditional medium Earth orbit satellite systems in mid-to-high latitude regions. This research provides an efficient solution for LEO navigation augmentation system design.