The Optimal Design of Baseline Configuration in GPS Networks by Using the Particle Swarm Optimisation Algorithm

Abstract The selection of the optimal GPS baselines can be performed by solving the geodetic second-order design (SOD) problem. In this paper, the particle swarm optimisation (PSO) algorithm, a stochastic global optimisation method, has been employed 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. PSO, which is an iterative-heuristic search algorithm in swarm intelligence, emulates collective behavior of bird flocking, fish schooling or bee swarming, to converge to the global optimum. The fundamentals of the algorithm are given. Then, the efficiency and the applicability of the algorithm are demonstrated with an example of GPS network. Our example shows that the PSO is practical because it does not produce negative or greater than maximum achievable weights of available instruments; it is effective because it yields networks that meet the optimisation criteria; and it is reliable because it converges to the global optimum of an objective function. It is also suitable for non-linear matrix functions that very often encountered in geodetic network optimisation.

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