Wireless and Mobile Network Architectures
Wireless and Mobile Network Architectures
A Comparison of Three Artificial Life Techniques for Reporting Cell Planning in Mobile Computing
IEEE Transactions on Parallel and Distributed Systems
Location Management in Mobile Computing
AICCSA '01 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications
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 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
Geometric particle swarm optimization for the sudoku puzzle
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Power-efficient epidemic information dissemination in sensor networks
BADS '09 Proceedings of the 2009 workshop on Bio-inspired algorithms for distributed systems
Injecting power-awareness into epidemic information dissemination in sensor networks
Future Generation Computer Systems
Differential evolution for solving the mobile location management
Applied Soft Computing
Solving the reporting cells problem using a scatter search based algorithm
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Swarm intelligence for traffic light scheduling: Application to real urban areas
Engineering Applications of Artificial Intelligence
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Mobile Location Management (MLM) is an important and complex telecommunication problem found in mobile cellular GSM networks. Basically, this problem consists in optimizing the number and location of paging cells to find the lowest location management cost. There is a need to develop techniques capable of operating with this complexity and used to solve a wide range of location management scenarios. Nature inspired algorithms are useful in this context since they have proved to be able to manage large combinatorial search spaces efficiently. The aim of this study is to assess the performance of two different nature inspired algorithms when tackling this problem. The first technique is a recent version of Particle Swarm Optimization based on geometric ideas. This approach is customized for the MLM problem by using the concept of Hamming spaces. The second algorithm consists of a combination of the Hopfield Neural Network coupled with a Ball Dropping technique. The location management cost of a network is embedded into the parameters of the Hopfield Neural Network. Both algorithms are evaluated and compared using a series of test instances based on realistic scenarios. The results are very encouraging for current applications, and show that the proposed techniques outperform existing methods in the literature.