Abstract
Location area (LA) management is a very important problem in mobile networks. In general, registration and paging costs are associated with tracking the current location of a mobile user. Considering minimizing the total of paging and registration costs as the main objective, the aim is to provide corresponding cell-to-switch and cell-to-LA assignments. This paper compares three well-known evolutionary algorithms to measure their suitability for solving location area management problems; these are genetic algorithms, multi-population genetic algorithms and memetic algorithms. To handle multiple objectives of paging and registration, a two-stage multi-population GA is developed. A memetic algorithm is introduced in order to improve the performance of a GA with the local search techniques. The effectiveness of these methods is shown for a number of test problems with different network size and characteristics.
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Karaoğlu, B., Topçuoğlu, H., Gürgen, F. (2005). Evolutionary Algorithms for Location Area Management. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2005. Lecture Notes in Computer Science, vol 3449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32003-6_18
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DOI: https://doi.org/10.1007/978-3-540-32003-6_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25396-9
Online ISBN: 978-3-540-32003-6
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