A metaheuristic perspective on the accuracy and stability of GNSS network adjustment

Global Navigation Satellite Systems (GNSS) are vital for surveying, mapping, and precise positioning, but adjusting these networks for optimal accuracy is complex. This study compares five optimisation methods, Particle Swarm Optimisation (PSO), Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), Backtracking Search Algorithm (BSA), and Weighted Differential Evolution (WDE), to the Least Squares (LS) method for improving GNSS network accuracy. Physics-based algorithms, particularly GSA and BSA, showed superior stability and consistency. GSA, in particular, achieved the highest accuracy and reliability, despite being more computationally intensive. These findings offer guidance for practitioners selecting optimisation methods for GNSS data.