Abstract
Ant Colony Optimization(ACO) has been used successfully to solve hard combinatorial optimization problems. This metaheuristic method is inspired by the foraging behavior of ants, which manage to establish the shortest routes from their nest to feeding sources and back. In this paper, we propose hybrid ACO approach to solve the Global Positioning System (GPS) surveying problem. In designing GPS surveying network, a given set of earth points must be observed consecutively (schedule). The cost of the schedule is the sum of the time needed to go from one point to another. The problem is to search for the best order in which this observation is executed. Minimizing the cost of this schedule is the goal of this work. Our results outperform those achieved by the best-so-far algorithms in the literature, and represent a new state of the art in this problem.
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Fidanova, S., Alba, E., Molina, G. (2010). Hybrid ACO Algorithm for the GPS Surveying Problem. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2009. Lecture Notes in Computer Science, vol 5910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12535-5_37
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DOI: https://doi.org/10.1007/978-3-642-12535-5_37
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