# An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets
## 6 Conclusions  
This study focused on the investigation of the performance of five optimization algorithms (ABC, ACO, GA, PSO, and SA) in selecting optimal GNSS satellite subsets and identifying the optimization algorithm with the most reliable, accurate, and cost-efficient features. The importance of this study is (i) the overall evaluation of the performance of the five optimization algorithms against the TM based on a complex optimization function (i.e., CNR-WGDOP) by combining the satellites' geometry and satellites' signal quality, (ii) and the investigation of how the algorithms' principles, settings, and parameters affect their performance.  
By comparing against the TM optimal solution, the evaluation for a GPS-only satellite constellation revealed that, generally, the ABC, ACO, and PSO algorithms had the highest accuracy in determining the optimal satellite subset and they were efficient in terms of computation time. However, the ABC algorithm is more practical with the fewest parameters to adjust, making the application of the optimization algorithm suitable for this problem. The evaluation of the ABC algorithm in the more complex problem of a multi-GNSS satellite constellation and even up to 31 available satellites revealed that the satellite selection accuracy of the ABC algorithm improves significantly by increasing (i.e., doubling) the values of population size and number of iterations, although this has an effect on the required computation time. The option of only doubling the population size parameter seems the most efficient as the accuracy of selecting optimal satellites ranged between 93% and 100% for the 24 h GNSS data period, with respect to the TM, without significantly increasing the required processing time. Also, it was shown that the TM is still the most efficient algorithm for selecting the optimal satellite combination from a single satellite system (i.e., GPS-only). However, this is not the case for multi-GNSS constellations with more simultaneously tracked satellites available, making the satellite selection problem too complex and time-consuming for the TM.  
Hence, it has been shown that the ABC algorithm can be suitable for optimizing the selection of tracked GNSS satellite constellations, which can be a very useful approach for GNSS technology to contribute to more reliable and accurate GNSS positioning performance. Also, the optimization of the tracked GNSS satellites is aligned with the recent interest in developing sustainable and low-cost GNSS solutions for monitoring applications of civil engineering infrastructure [67] and geohazards [68] by optimizing and limiting the number of required GNSS satellites. This can result in a reduction in the GNSS data size and improve the ease of data transfer. However, further investigation is needed to shed more light on how to make the application of these algorithms to optimizing satellite constellations even more efficient in terms of processing time and performance. Their performance also needs to be evaluated in more complicated satellite selection scenarios where the number of possible satellite subsets exceeds 300 million by increasing the number of tracking satellites to more than 31 when including satellites from other systems, such as BeiDou.  
Methodology, A.A., P.P. and R.R.-P.; investigation, A.A.; software, A.A.; formal analysis, A.A.; supervision, P.P. and R.R.-P.; validation, P.P. and R.R.-P.; writing--original draft preparation, A.A.; writing--review and editing, P.P. and R.R.-P. All authors have read and agreed to the published version of the manuscript.  
Taibah University and Saudi Arabia Cultural Bureau (SACB).  
Data Availability Statement: Data can be provided by the authors upon request.  
The authors declare no conflict of interest.