An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets
Abstract
:1. Introduction
2. GNSS Satellite Constellation Determination
2.1. Weighted Geometric Dilution of Precision (WGDOP)
2.2. Traditional Method (TM)
2.3. Optimization Algorithms
2.3.1. Artificial Bee Colony (ABC)
2.3.2. Ant Colony Optimization (ACO)
2.3.3. Genetic Algorithm (GA)
2.3.4. Particle Swarm Optimization (PSO)
2.3.5. Simulated Annealing (SA)
3. Methodology of Selecting Tracked GNSS Satellites Based on Optimization Algorithms
Algorithm | Notation | Meaning | Value | References |
---|---|---|---|---|
ABC | D | The problem dimensions, indicating the satellite constellation size | ||
maxIter | The maximum number of iterations | 100 | [26,28,61,62] | |
pSize | The population size | 100 | [22,26,45,63] | |
limit | The abandonment limit | D × pSize | [64] | |
ACO | maxIter | The maximum number of iterations | 100 | |
pSize | The population size | 100 | ||
ρ | The evaporation rate | 0.5 | [22,40,45] | |
α | The relative importance of the trail | 1 | ||
β | The relative importance of the visibility | 0 | ||
Q | The quantity of trails laid by ants | 100 | ||
GA | maxIter | The maximum number of iterations | 100 | |
pSize | The population size | 100 | ||
Pc | The probability of crossover | 0.8 | [45,61] | |
Pm | The probability of mutation | 12 | ||
PSO | maxIter | The maximum number of iterations | 100 | |
pSize | The population size | 100 | ||
Wmax | The initial weights | 0.9 | [45,62,65] | |
Wmin | The final weights | 0.4 | ||
C1 | The personal acceleration coefficient | 2 | ||
C2 | The social acceleration coefficient | 2 | ||
SA | maxIter | The maximum number of iterations | 1000 | [45,60] |
pSize | The population size | 1 | ||
T0 | The initial temperature | 2000 | ||
Tf | The final temperature | 0.01 | ||
α | The cooling factor | 0.975 |
4. GNSS Data
5. Results and Discussion
5.1. GPS Optimal Satellites
5.2. Multi-GNSS Optimal Satellites
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Setting | maxIter | pSize | Limit |
---|---|---|---|
1 | 100 | 100 | D × pSize |
2 | 100 | 200 | D × pSize |
3 | 200 | 200 | D × pSize |
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Alluhaybi, A.; Psimoulis, P.; Remenyte-Prescott, R. An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets. Remote Sens. 2024, 16, 1794. https://doi.org/10.3390/rs16101794
Alluhaybi A, Psimoulis P, Remenyte-Prescott R. An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets. Remote Sensing. 2024; 16(10):1794. https://doi.org/10.3390/rs16101794
Chicago/Turabian StyleAlluhaybi, Abdulaziz, Panos Psimoulis, and Rasa Remenyte-Prescott. 2024. "An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets" Remote Sensing 16, no. 10: 1794. https://doi.org/10.3390/rs16101794
APA StyleAlluhaybi, A., Psimoulis, P., & Remenyte-Prescott, R. (2024). An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets. Remote Sensing, 16(10), 1794. https://doi.org/10.3390/rs16101794