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Efficient Evolutionary Algorithms for GPS Satellites Classification

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In this paper, the classification methods based on evolutionary algorithms (EAs) for choosing optimum satellites subset in GPS receivers are proposed. These methods use EAs to achieve best optimum answers. Four EAs including simulating annealing, genetic algorithm, particle swarm optimization and bee algorithm are used and compared with each other. These methods try to find the subset which has the lowest geometric dilution of precision (GDOP). The GDOP is a measure of the overall uncertainty in a GPS position solution. The most important advantage of these methods is that there is no need to calculate inverse matrix; therefore, they reduce computational burden. Also, in contrast with classification methods based on neural networks, these algorithms do not need training process, so they are very fast. The proposed methods are simulated and validated by a software simulation. The simulation results demonstrate that bee algorithm has greater accuracy and calculation time than other methods, so that it can improve classification accuracy of GPS satellites about 99.86 %.

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Correspondence to M. R. Mosavi.

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Mosavi, M.R., Shiroie, M. Efficient Evolutionary Algorithms for GPS Satellites Classification. Arab J Sci Eng 37, 2003–2015 (2012).

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