The Use of a Hopfield Neural Network in Solving the Mobility Management Problem
ICPS '04 Proceedings of the The IEEE/ACS International Conference on Pervasive Services
A Simulated Annealing Approach for Mobile Location Management
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 6 - Volume 07
A Genetic Algorithm for Finding Optimal Location Area Configurations for Mobility Management
LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Differential evolution for solving the mobile location management
Applied Soft Computing
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Computationally efficient algorithms for location area planning in future cellular systems
Computer Communications
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The Public Land Mobile Networks (PLMN) are designed to provide anywhere, any kind, and anytime services to either static or moving users, therefore mobile location management is a fundamental tool in these systems. One of the techniques used in mobile location management is the location areas strategy, which set out the problem as an optimization problem with two costs, location update and paging. In this paper we resort to a multi-objective evolutionary algorithm, Non-dominated Sorting Genetic Algorithm II (NSGA-II), for finding quasi-optimal solutions of this optimization problem. At present, there is not any previous work that addresses the problem in a multi-objective manner, so we compare our results with those obtained by mono-objective algorithms from other authors. Results show that, for this problem, better solutions are achieved when each objective is treated separately.