@misc{10481/82911,
year = {2023},
month = {3},
url = {https://hdl.handle.net/10481/82911},
abstract = {The massive deployment of base stations is one of the key pillars of the fifth generation (5G) of mobile
communications. However, this network densification entails high energy consumption that must be addressed
to enhance the sustainability of this industry. This work faces this problem from a multi-objective optimization
perspective, in which both energy efficiency and quality of service criteria are taken into account. To do
so, several newly problem-specific operators have been designed so as to engineer hybrid multi-objective
evolutionary metaheuristics (MOEAs) that bring expert knowledge of the domain to the search of the
algorithms. These hybrid approaches have been able to improve upon canonical versions of the algorithms,
clearly showing the contributions of our approach. Furthermore, this paper tests the hypothesis that the
hybridization using several of those problem-specific operators simultaneously can enhance the search of
MOEAs that are endowed only with a single one.},
organization = {Spanish Ministry of
Science and Innovation via grant PID2020-112545RB-C54},
organization = {European
Union NextGenerationEU/PRTR under grants TED2021-131699BI00},
organization = {TED2021-129938B-I00 (MCIN/AEI/10.13039/501100011033,
FEDER)},
organization = {Andalusian PAIDI program with grants A-TIC-608-
UGR20, P18.RT.4830},
organization = {PYC20-RE-012-UGR},
organization = {Supercomputing and Bioinformatics Center of the Universidad de
Málaga},
organization = {Universidad de Málaga/CBUA},
publisher = {Elsevier},
keywords = {Problem specific operator},
keywords = {Hybridization},
keywords = {Multi-objective optimization},
keywords = {Ultra-dense networks},
keywords = {5G},
title = {Designing problem-specific operators for solving the Cell Switch-Off problem in ultra-dense 5G networks with hybrid MOEAs},
doi = {10.1016/j.swevo.2023.101290},
author = {Galeano-Brajones, Jesús and Valenzuela Valdes, Juan Francisco},
}