A multi-strategy optimizer for energy minimization of multi-UAV-assisted mobile edge computing

Disasters in remote areas often cause damage to communication facilities, which presents significant challenges for rescue efforts. As flexible mobile devices, unmanned aerial vehicles (UAVs) can provide temporary network services to address this issue. This paper studies the use of UAVs as mobile b...

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Bibliographic Details
Published inSwarm and evolutionary computation Vol. 91
Main Authors Chen, Yang, Pi, Dechang, Yang, Shengxiang, Xu, Yue, Wang, Bi, Wang, Yintong
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.12.2024
Subjects
Energy minimization
Multi-strategy optimizer
Swarm intelligence
UAV-assisted MEC
Multi-strategy optimizer
UAV-assisted MEC
Swarm intelligence
Energy minimization
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Summary:Disasters in remote areas often cause damage to communication facilities, which presents significant challenges for rescue efforts. As flexible mobile devices, unmanned aerial vehicles (UAVs) can provide temporary network services to address this issue. This paper studies the use of UAVs as mobile base stations to offer offload computing services for disaster relief devices in affected areas. To ensure reliable communication between disaster relief devices and UAVs, we construct a multi-UAV-assisted mobile edge computing (MEC) system with the objective of minimizing system energy consumption. Inspired by swarm intelligence principles, we propose a multi-strategy optimizer (MSO) that defines various population search functions and employs superior neighborhood methods for population updates. Experimental results demonstrate that MSO achieves superior system energy efficiency and exhibits greater stability compared to several state-of-the-art swarm intelligence algorithms.
ISSN:2210-6502
DOI:10.1016/j.swevo.2024.101748