The satellite-based Global Positioning System (GPS) has an impact on
all related fields in geoscience and engineering, in particular, on
surveying work in determining locations and changes in locations
within short observational periods and over long distances with
unprecedented accuracy. On other hand, the complexity of observing
GPS networks increases with their size and become highly difficult to
solve effectively. The network in GPS can be defined as a number of
stations which are co-ordinated by placing receivers on them to
determine sessions between them. The problem is to search for the best
order in which these sessions can be organised to give the best
schedule of minimal cost to observe all sessions. Exact algorithms can
solve only small networks and are not practical as the size of the
network increases. Hence, it is crucial to have approximate
techniques which can provide an optimal or near-optimal schedule for
large networks in reasonable amount of computational time.
Computational results are presented to show the effectiveness and
performance of the developed Simulated Annealing and Tabu Search
metaheuristic techniques with respect to schedule quality and the
computational effort using different types and sizes of GPS networks.
Other metaheuristics such Ant Colony System could explore the
solution space more effectively and provide good results.
Global Positioning System (GPS), Metaheuristics, Network,
Simulated Annealing, Tabu Search, Ant Colony System.
A. Leick. (1995)
GPS Satellite Surveying. Wiley, Chichester, UK. 2nd edition.
P. Dare and H. Saleh. (2000)
GPS Network Design: Logistic Solution using optimal and near-optimal methods,
Journal of Geodesy, 74:467-478.
H. A. Saleh. (1999)
A Heuristic Approach to the design of GPS Networks. Ph.D. Thesis. School of Surveying, University of East London, Dagenham, Essex, UK.
H. Saleh and P. Dare. (2001)
Effective Heuristics for the GPS survey Network of Malta: Simulated Annealing and Tabu Search techniques,
Journal of Heuristics, 7(6):533-549.
M. Dorigo, V. Maniezzo, and A. Colorni. (1996)
The ant system: Optimization by a colony of cooperating agents,
IEEE Transactions on Systems, Man, and Cybernetics-Part B, 26(1):29-41.