2026-06-02T02:14:41Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/263382026-02-03T11:01:10Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Galeano-Brajones, Jesús author Luna-Valero, Francisco author Carmona Murillo, Javier author Zapata Cano, Pablo Helio author Valenzuela-Valdés, Juan Francisco author 2023 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. Jesús Galeano-Brajones, Francisco Luna-Valero, Javier Carmona-Murillo, Pablo H. Zapata Cano, Juan F. Valenzuela-Valdés, Designing problem-specific operators for solving the Cell Switch-Off problem in ultra-dense 5G networks with hybrid MOEAs, Swarm and Evolutionary Computation, Volume 78, 2023, 101290, ISSN 2210-6502, https://doi.org/10.1016/j.swevo.2023.101290. https://hdl.handle.net/10630/26338 https://doi.org/10.1016/j.swevo.2023.101290 Optimización multidisciplinar Designing problem-specific operators for solving the Cell Switch-Off problem in ultra-dense 5G networks with hybrid MOEAs