Design of global positioning system (GPS) networks using different artificial intelligence techniques
The selection of optimal GPS baselines can be realized by solving the geodetic second-order design (SOD) problem. Basically, there are two techniques to be used for selecting optimal baselines in GPS network, namely traditional techniques and artificial techniques. Traditional techniques include the method of trial and error and the analytical method, while artificial methods include both local and global optimization techniques. The global optimization techniques, such as Genetic Algorithms (GAs), Simulated Annealing (SA) method, Particle Swarm Optimization (PSO) Algorithm, and Butterfly Optimization Algorithm (BOA) have been recently used in geodesy. In the current study, (BOA) has been used for the selection of the optimal GPS baselines to be measured in the field that will meet the postulated criterion matrix, at a reasonable cost. The work has been executed on a pre-designed GPS network, where the number of baselines, to be observed to attain high accuracy, has been already determined. The results of this paper were more efficient than the results of the traditional methods by 19.2% where it was better than the artificial methods in terms of length, whereas it enhanced (SA) method by 21.7% and (PSO) method by 4.6% . Consequently, the use of the BOA is effective and applicable.