Evolutionary algorithms for location area management

Authors:
Bahar Karaoğlu;Haluk Topçuoğlu;Fikret Gürgen
Affiliations:
Department of Computer Engineering, Boğaziçi University, Bebek, Istanbul, Turkey;Department of Computer Engineering, Marmara University, Goztepe, Istanbul, Turkey;Department of Computer Engineering, Boğaziçi University, Bebek, Istanbul, Turkey
Venue:
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Year:
2005

Citing 6
Cited 3

New ideas in optimization

New ideas in optimization
Evolving Cellular Automata for Location Management in Mobile Computing Networks

IEEE Transactions on Parallel and Distributed Systems
A Comparison of Three Artificial Life Techniques for Reporting Cell Planning in Mobile Computing

IEEE Transactions on Parallel and Distributed Systems
Sequential and multi-population memetic algorithms for assigning cells to switches in mobile networks

Computer Networks: The International Journal of Computer and Telecommunications Networking
Location area planning and cell-to-switch assignment in cellular networks

IEEE Transactions on Wireless Communications
Evolutionary approach to optimize the assignment of cells to switches in personal communication networks

Computer Communications

A hybrid grouping genetic algorithm for the registration area planning problem

Computer Communications
Location areas planning optimization in mobile networks

PDCS '07 Proceedings of the 19th IASTED International Conference on Parallel and Distributed Computing and Systems
Soft computing approach for location management problem in wireless mobile environment

SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

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.