Evidence map›Paper›PMID 40841740›Full record

ArticleScientific reports2025

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

Chao Qin, Yanbin Gao, Yihuan Wang

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Chao QinCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, China. max_qinchao@163.com.
Yanbin GaoCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, China.
Yihuan WangThe 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang, 050081, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Constellation optimizationGenetic algorithmLow earth orbit satellite constellationNavigation precisionSatellite visibility

Identifiers

PMID40841740
PMCPMC12371013

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.