Abstract
Metaheuristic techniques, which are based on ideas of Artificial Intelligence, are among the best methods for solving computationally the GPS surveying network problem. In this paper, the ant colony optimization metaheuristic, which is inspired by the behavior of real ant colonies, is developed to efficiently provide a general framework for optimizing GPS surveying networks. In this framework, a set of ants co-operate together using an indirect communication procedure to find good GPS observation schedules. A GPS surveying network can be defined as a set of stations, co-ordinated by a series of sessions formed by placing receivers on the stations. The problem is to search for the best order in which to observe these sessions to give the best schedule at minimum cost. Computational results obtained by applying the proposed technique on several networks, with known and unknown optimal schedules, prove the effectiveness of the proposed metaheuristic technique to solve the GPS surveying network problem.
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Acknowledgement
This research was supported by both the Syrian Ministry of Higher Education and by a Marie Curie Fellowship awarded to Hussain Saleh. Also, this work was supported by the "Metaheuristics Network", a Research Training Network funded by the Improving Human Potential programme of the CEC. Thanks are extended to Dr. Stefka Fidanova and the reviewers for the many useful comments on the first version of this paper.
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Saleh, H.A. An Artificial Intelligent design for GPS Surveying Networks. GPS Solutions 7, 101–108 (2003). https://doi.org/10.1007/s10291-003-0056-4
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DOI: https://doi.org/10.1007/s10291-003-0056-4