Abstract
The massive deployment of small base stations is one of the main pillars for the new generations of mobile networks to meet the expected growing in data traffic demands. This densification entails high energy consumption that needs to be minimized to ensure system sustainability in a context of reduced environmental impact. To address this issue, optimization algorithms that will rely on metaheuristics can be used due to the complexity and the large instance size of the problem. Therefore, it is a multi-objective optimization problem in which not only the energy efficiency criteria is taken into account, but also the service provided to the users in terms of capacity is considered. In this context, the aim of this work is to evaluate the performance of Binary Particle Swarm Optimization (BPSO) in solving this multi-objective problem, using a V-shaped function to deal with binary codification. The performance of our proposed solution is compared with the results obtained by MOCell and NSGA-II in our previous works. In addition, the performance of the hybridization with specific operators proposed in one of our previous works is tested. The research showed that the hybridization brought very significant benefits to the algorithm’s searches.
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Notes
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The source code is available at https://github.com/galeanobra/CSO_BPSO.git
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Acknowledgements
This research was funded in part by the Spanish Ministry of Science and Innovation, grant number PID2020-112545RB-C54, and the Regional Government of Extremadura, Spain, grant numbers IB18003 and GR21097.
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Espinosa-Martínez, J.J., Galeano-Brajones, J., Carmona-Murillo, J., Luna, F. (2022). Binary Particle Swarm Optimization for Selective Cell Switch-Off in Ultra-Dense 5G Networks. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2022. Lecture Notes in Computer Science, vol 13491. Springer, Cham. https://doi.org/10.1007/978-3-031-20176-9_23
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