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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. 相似文献
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An approach based on the behaviour of human genome is developed to efficiently provide a general framework for optimizing
the use of space technology in surveying networks design. The developed approach attempts to apply the successful self-organizing
principles based upon the biological evolution to artificial intelligence. It mimics the phenomena of natural selection observed
in nature to achieve its goals by continuously adopting a population of candidate solutions and improving its performance
over successive generations. The goal of adaptation is to find the best solution that optimizes the design of a surveying
network based on the use of satellite observations. This 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 for observing these sessions
to give the best observation schedule at minimum cost. The obtained results prove the effectiveness of the developed technique
in term of solution quality and computational efforts.
The Corresponding Author in formed me that his address is likely to change soon. Please contact me, leick@spatial.maine.edu
for the latest on this change. 相似文献
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