Article
Journal/Revue :
Expert Systems with Applications
ISSN : 0957-4174
Publisher :
Informations
Période : September 2018
Volume : 106
Pages : 290-304
Détails
A Stop-and-Start Adaptive Cellular Genetic Algorithm for Mobility Management of GSM-LTE Cellular Network Users
Zakaria Abdelmoiz Dahi Enrique Alba Amer Draa
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.
Mots clés :
Cellular Networks Cellular Genetic Algorithms Adaptation
Citation ref. :
misc-lab-119
DOI :
10.1016/j.eswa.2018.02.041
Lien :
Texte intégral
ACM :
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 -