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

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation Pub Date : 2024-10-11 DOI:10.1016/j.swevo.2024.101748

Yang Chen , Dechang Pi , Shengxiang Yang , Yue Xu , Bi Wang , Yintong Wang

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引用次数: 0

Abstract

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.

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多无人机辅助移动边缘计算能量最小化的多策略优化器

偏远地区发生灾害时，通信设施往往会遭到破坏，这给救援工作带来了巨大挑战。作为灵活的移动设备，无人飞行器（UAV）可以提供临时网络服务来解决这一问题。本文研究利用无人飞行器作为移动基站，为灾区的救灾设备提供卸载计算服务。为确保救灾设备与无人机之间的可靠通信，我们构建了一个多无人机辅助移动边缘计算（MEC）系统，目标是最大限度地降低系统能耗。受蜂群智能原理的启发，我们提出了一种多策略优化器（MSO），它定义了各种种群搜索函数，并采用卓越的邻域方法进行种群更新。实验结果表明，与几种最先进的群智能算法相比，MSO 实现了更高的系统能效，并表现出更强的稳定性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

16.00

自引率

12.00%

发文量

169

期刊介绍： Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.