Abstract
The short-time aggregation of human traffic places high demands on the communication capacity of cellular networks. The deployment of expensive permanent infrastructure without continuous high traffic is uneconomical, and the problem poses a challenge. In this study, a green mall traffic model based on mobile base stations with a dynamic sleep strategy is proposed for surges of shopping mall traffic. The model is addressed through a modified improved differential evolution (MIDE) algorithm based on the original improved differential evolution (IDE) algorithm. The algorithm has two sets of mutation and restart policies adapted to different traffic volumes, and can dynamically adjust according to the traffic volume. The effectiveness of the algorithm is verified by simulation experiments. Compared with the traditional differential evolution (DE) algorithm and the DE series algorithms recently published in Swarm and Evolutionary Computation, a journal with high impact factors, MIDE can effectively optimize the system model and improve its energy efficiency, saving 1.1%–56.4% in simulation experiments.
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Acknowledgments
We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.
This research was supported by National Natural Science Foundation of China, (grant no. 61663046,61876166); Additional funding was supplied by a grant from the Open Foundation of Key Laboratory of Software Engineering of Yunnan Province (grant no.2020SE308, 2020SE309).
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Sun, X., Zhang, T., Xu, J. et al. Energy efficiency-driven mobile base station deployment strategy for shopping malls using modified improved differential evolution algorithm. Appl Intell 53, 1233–1253 (2023). https://doi.org/10.1007/s10489-022-03358-x
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DOI: https://doi.org/10.1007/s10489-022-03358-x