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
Clustering techniques for dynamic mobility management
Proceedings of the 4th ACM international workshop on Mobility management and wireless access
A Simulated Annealing approach for mobile location management
Computer Communications
Clustering techniques for dynamic location management in mobile computing
Journal of Parallel and Distributed Computing
New research in nature inspired algorithms for mobility management in GSM networks
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Differential evolution for solving the mobile location management
Applied Soft Computing
A combined genetic-neural algorithm for mobility management
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Fuzzy online location management in mobile computing environments
Journal of Parallel and Distributed Computing
A multi-objective approach to solve the location areas problem
TPNC'12 Proceedings of the First international conference on Theory and Practice of Natural Computing
Solving the location areas scheme in realistic networks by using a multi-objective algorithm
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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This work presents a new approach to solve the location management problem by using the location areas approach. Hopfield Neural Network is used in this work to find the optimal configuration of location areas in a mobile network. Toward this end, the location areas configuration of the network is modeled so that the mobility management cost of the system is related to the energy value of this artificial neural network. Since a pure Hopfield Neural Network was not efficient enough to be used in solving this problem, some modifications applied to it to make it a reasonable approach. Basically, these modifications deal with randomness of selection in manipulating the cells during the solving process. Simulation results are very promising and they lead to network configurations that are unexpected.