The optimisation of the user tracking process is one of the most challenging tasks in today’s advanced cellular networks. In this paper, we propose a new low-complexity adaptive cellular genetic algorithm to solve this problem. The proposed approach uses a torus-like structured population of candidate solutions and regulates interactions inside it by using a bi-dimensional neighbourhood. It also automatically adapts the algorithm’s parameters and regenerates the algorithm’s population using two algorithmically-light operators. In order to draw reliable conclusions and perform an encompassing assessment, extensive experiments have been conducted on 25 differently-sized realistic networks. The proposed approach has been compared against 26 state-of-the-art algorithms previously designed to solve the mobility management problem, and a thorough statistical analysis of results has been performed. The obtained results have shown that our proposal is more efficient and algorithmically less complex than most of the state-of-the-art solvers.
Z. A. Dahi, E. Alba and A. Draa. 2018. A Stop-and-Start Adaptive Cellular Genetic Algorithm for Mobility Management of GSM-LTE Cellular Network Users. Expert Systems with Applications, 106 (September 2018), Elsevier, 290-304. DOI: https://doi.org/10.1016/j.eswa.2018.02.041.
APA :
Dahi, Z. A., Alba, E. & Draa, A. (2018, September). A Stop-and-Start Adaptive Cellular Genetic Algorithm for Mobility Management of GSM-LTE Cellular Network Users. Expert Systems with Applications, 106, Elsevier, 290-304. DOI: https://doi.org/10.1016/j.eswa.2018.02.041
IEEE :
Z. A. Dahi, E. Alba and A. Draa, "A Stop-and-Start Adaptive Cellular Genetic Algorithm for Mobility Management of GSM-LTE Cellular Network Users". Expert Systems with Applications, vol. 106, Elsevier, pp. 290-304, September, 2018. DOI: https://doi.org/10.1016/j.eswa.2018.02.041.
BibTeX :
@article{misc-lab-119, author = {Dahi, Zakaria Abdelmoiz and Alba, Enrique and Draa, Amer}, title = {A Stop-and-Start Adaptive Cellular Genetic Algorithm for Mobility Management of GSM-LTE Cellular Network Users}, journal = {Expert Systems with Applications}, volume = {106}, issn = {0957-4174}, pages = {290--304}, publisher = {Elsevier}, year = {2018}, month = {September}, doi = {10.1016/j.eswa.2018.02.041}, url = {https://www.sciencedirect.com/science/article/pii/S0957417418301301?via%3Dihub}, keywords = {Cellular Networks, Cellular Genetic Algorithms, Adaptation} }
RIS :
TI - A Stop-and-Start Adaptive Cellular Genetic Algorithm for Mobility Management of GSM-LTE Cellular Network Users AU - Z. A. Dahi AU - E. Alba AU - A. Draa PY - 2018 SN - 0957-4174 JO - Expert Systems with Applications VL - 106 SP - 290 EP - 304 PB - Elsevier AB - The optimisation of the user tracking process is one of the most challenging tasks in today’s advanced cellular networks. In this paper, we propose a new low-complexity adaptive cellular genetic algorithm to solve this problem. The proposed approach uses a torus-like structured population of candidate solutions and regulates interactions inside it by using a bi-dimensional neighbourhood. It also automatically adapts the algorithm’s parameters and regenerates the algorithm’s population using two algorithmically-light operators. In order to draw reliable conclusions and perform an encompassing assessment, extensive experiments have been conducted on 25 differently-sized realistic networks. The proposed approach has been compared against 26 state-of-the-art algorithms previously designed to solve the mobility management problem, and a thorough statistical analysis of results has been performed. The obtained results have shown that our proposal is more efficient and algorithmically less complex than most of the state-of-the-art solvers. KW - Cellular Networks KW - Cellular Genetic Algorithms KW - Adaptation DO - 10.1016/j.eswa.2018.02.041 UR - https://www.sciencedirect.com/science/article/pii/S0957417418301301?via%3Dihub ID - misc-lab-119 ER -