[1]方浩添,田乐,郭茂祖.基于多群体混合智能优化算法的卸载决策寻优方法[J].智能系统学报,2024,19(6):1573-1583.[doi:10.11992/tis.202312042]
FANG Haotian,TIAN Le,GUO Maozu.Unloading decision optimization method based on multi-population hybrid intelligent optimization algorithm[J].CAAI Transactions on Intelligent Systems,2024,19(6):1573-1583.[doi:10.11992/tis.202312042]
点击复制
FANG Haotian,TIAN Le,GUO Maozu.Unloading decision optimization method based on multi-population hybrid intelligent optimization algorithm[J].CAAI Transactions on Intelligent Systems,2024,19(6):1573-1583.[doi:10.11992/tis.202312042]
基于多群体混合智能优化算法的卸载决策寻优方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第6期
页码:
1573-1583
栏目:
人工智能院长论坛
出版日期:
2024-12-05
- Title:
- Unloading decision optimization method based on multi-population hybrid intelligent optimization algorithm
- Keywords:
- moving edge computing; calculating offloading; artificial fish swarm algorithm; artificial colony algorithm; self-similar queuing model; gaussian attenuation function; particle swarm optimization; inertia weight factor
- 分类号:
- TP393
- 摘要:
- 在移动边缘计算的网络架构中，为权衡降低计算应用卸载的能耗与时延，引入卸载决策控制器，并通过卸载决策寻优算法得到最优卸载决策。结合人工蜂群算法和人工鱼群算法提出新的人工蜂-鱼群（artificial bee colony-fish swarm， ABC-FS）算法，在此基础上引入高斯衰减函数将算法参数由静态变为动态，并将改进粒子群算法的惯性权重因子引入算法中，从而得到一种多群体混合智能优化算法；设计联合优化时延与能耗的目标函数，再依据泊松概率进行仿真实验。仿真实验结果表明，提出的卸载策略寻优算法，与多组对照组相比，收敛速度更快，且在多接入边缘计算的场景下能权衡降低系统中任务卸载的总时延与总能耗。
- Abstract:
- In the network architecture of mobile edge computing, an offloading decision controller was introduced to balance the reduction of energy consumption and delay. This controller obtains the optimal offloading decision through an offloading decision optimization algorithm. A new ABC–FS algorithm was proposed by combining the artificial bee colony (ABC) algorithm and the artificial fish swarm (FS) algorithm. Additionally, a Gaussian decay function was introduced to transition the algorithm parameters from static to dynamic, and the inertia weight factor of the improved particle swarm optimization algorithm was incorporated, creating a multi-population hybrid intelligent optimization algorithm. Finally, an objective function that jointly optimizes delay and energy consumption was designed, and simulation experiments were conducted using Poisson probability. Simulation results show that the proposed offloading strategy optimization algorithm achieves faster convergence speed compared to several benchmark methods and effectively balances the reduction of total task offloading delay and total energy consumption in multi-access edge computing scenarios.
- 参考文献/References:
-
[1] MACH P, BECVAR Z. Mobile edge computing: a survey on architecture and computation offloading[J]. IEEE communications surveys & tutorials, 2017, 19(3): 1628-1656.
[2] 薛建强, 史彦军, 李波. 面向无人集群的边缘计算技术综述[J]. 兵工学报, 2023, 44(9): 2546-2555.
XUE Jianqiang, SHI Yanjun, LI Bo. A review of edge computing technology for unmanned swarms[J]. Acta armamentarii, 2023, 44(9): 2546-2555.
[3] 张冰洁, 杨彦红, 曹少中. 面向多接入边缘计算的计算卸载方案研究综述[J]. 计算机科学与探索, 2023, 17 (9): 2030-2046.
ZHANG Bingjie, YANG Yanhong, CAO Shaozhong. Review of computing offloading schemes for multi-access edge computing[J]. Journal of frontiers of computer science and technology, 2023, 17 (9): 2030-2046.
[4] 孔珊, 柴争义, 郑玉琦. 基于进化多任务多目标优化的边缘计算任务卸载[J]. 计算机应用研究, 2024, 41(4): 1164-1170.
KONG Shan, CHAI Zhengyi, ZHENG Yuqi. Task offload in edge computing based on evolutionary multi task multi-objective optimization[J]. Application research of computers, 2024, 41(4): 1164-1170.
[5] KARABOGA D. An idea based on honey bee swarm for numerical optimization[R]. Technical report TR06, Erciyes University, 2005(TR06) .
[6] 李晓磊. 一种新型的智能优化方法-人工鱼群算法[D]. 杭州: 浙江大学, 2003.
LI Xiaolei. A new intellingent optimization Method-Artificial fish school algorithm[D]. Hangzhou: Zhejiang University, 2003.
[7] EBERHART R, KENNEDY J. A new optimizer using particle swarm theory[C]//MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Nagoy: IEEE, 2002: 39-43.
[8] KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of ICNN’95 - International Conference on Neural Networks. Perth: IEEE, 2002: 1942-1948.
[9] 郝昊, 杨树杰, 张玮. 算网融合下时间连续的计算任务卸载机制[J]. 计算机研究与发展, 2023, 60(4): 735-749.
HAO Hao, YANG Shujie, ZHANG Wei. Time-continuous computing task offloading mechanism for computing and network convergence[J]. Journal of computer research and development, 2023, 60(4): 735-749.
[10] 姜玉龙, 东方, 郭晓琳, 等. 算力网络环境下基于势博弈的工作流任务卸载优化机制[J]. 计算机研究与发展, 2023, 60(4): 797-809.
JIANG Yulong, DONG Fang, GUO Xiaolin, et al. Potential game based workflow task offloading optimization mechanism in computing power network[J]. Journal of computer research and development, 2023, 60(4): 797-809.
[11] ZHANG Y, ZHOU X , TENG Y, et al. Resource allocation for multi-user mobile edge computing system: machine learning approaches[C]//Proceedings of the 2018 International Conference on Computational Science and Computational Intelligence. Las Vegas: IEEE, 2018: 794-799.
[12] LYU Xinchen, TIAN Hui, JIANG Li, et al. Selective offloading in mobile edge computing for the green Internet of Things[J]. IEEE network, 2018, 32(1): 54-60.
[13] 孙伟峰, 张渊櫆, 江贺, 等. 基于双层Stackelberg博弈的MEC计算卸载方案[J]. 软件学报, 2023, 34(9): 4275-4293.
SUN Weifeng , ZHANG Yuankui, JIAN He, et al. Computation offloading scheme based on two-layer stackelberg game for multi-access edge computing[J]. Journal of software, 2023, 34(9): 4275-4293.
[14] 张祥俊, 伍卫国, 张弛, 等. 面向移动边缘计算网络的高能效计算卸载算法[J]. 软件学报, 2023, 34(2): 849-867.
ZHANG Xiangjun, WU Weiguo, ZHANG Chi, et al. Energy-efficient computing offloading algorithm for mobile edge computing network[J]. Journal of software, 2023, 34(2): 849-867.
[15] 邝祝芳, 陈清林, 李林峰, 等. 基于深度强化学习的多用户边缘计算任务卸载调度与资源分配算法[J]. 计算机学报, 2022, 45(4): 812-824.
KUANG Zhufang, CHEN Qinglin, LI Linfeng, et al. Multi-user edge computing task offloading scheduling and resource allocation based on deep reinforcement learning[J]. Chinese journal of computers, 2022, 45(4): 812-824.
[16] KAR B, YAHYA W, LIN Y D, et al. A survey on offloading in federated cloud-edge-fog systems with traditional optimization and machine learning[EB/OL]. (2022-02-22)[2022-12-12]. http://arxiv.org/abs/2202.10628.
[17] FENG Siling, CHEN Yinjie, ZHAI Qianhao, et al. Optimizing computation offloading strategy in mobile edge computing based on swarm intelligence algorithms[J]. EURASIP journal on advances in signal processing, 2021, 2021(1): 36.
[18] 张文柱, 余静华. 移动边缘计算中基于云边端协同的任务卸载策略[J]. 计算机研究与发展, 2023, 60(2): 371-385.
ZHANG Wenzhu, YU Jinghua. Task offloading strategy in mobile edge computing based on cloud-edge-end cooperation[J]. Journal of computer research and development, 2023, 60(2): 371-385.
[19] 王亭惠, 陈桂芬. 基于DP-HAFS人工鱼群算法的移动边缘计算卸载策略[J]. 计算机应用研究, 2023, 40(4): 1184-1188.
WANG Tinghui, CHEN Guifen. Mobile edge computing offloading strategy based on DP-HAFS algorithm[J]. Application research of computers, 2023, 40(4): 1184-1188.
[20] 孔小爽, 袁健. 基于鸟群人工鱼群算法的区块链移动边缘计算卸载模型[J]. 电子科技, 2024, 37(8): 26-33.
KONG Xiaoshuang, YUAN Jian. Blockchain mobile edge computing offloading model based on bird swarm artificial fish swarm algorithm[J]. Electronic science and technology, 2024, 37(8): 26-33.
[21] 齐小刚, 单明媚, 张皓然. 软件定义网络故障诊断综述[J]. 智能系统学报, 2023, 18(4): 662-675.
QI Xiaogang, SHAN Mingmei, ZHANG Haoran. Summary of software-defined networking fault diagnosis[J]. CAAI transactions on intelligent systems, 2023, 18(4): 662-675.
[22] 章呈瑞, 柯鹏, 尹梅. 改进人工蜂群算法及其在边缘计算卸载的应用[J]. 计算机工程与应用, 2022, 58(7): 150-161.
ZHANG Chengrui, KE Peng, YIN Mei. Improved artificial bee colony algorithm and its application in edge computing offloading[J]. Computer engineering and applications, 2022, 58(7): 150-161.
[23] 安家乐, 刘晓楠, 何明, 等. 量子群智能优化算法综述[J]. 计算机工程与应用, 2022, 58(7): 31-42.
AN Jiale, LIU Xiaonan, HE Ming, et al. Survey of quantum swarm intelligence optimization algorithm[J]. Computer engineering and applications, 2022, 58(7): 31-42.
[24] 张毅, 杨光辉, 花远红. 基于改进人工鱼群算法的机器人路径规划[J]. 控制工程, 2020, 27(7): 1157-1163.
ZHANG Yi, YANG Guanghui, HUA Yuanhong. Robot path planning based on improved artificial fish swarm algorithm[J]. Control engineering of China, 2020, 27(7): 1157-1163.
[25] 王生亮, 刘根友. 一种非线性动态自适应惯性权重PSO算法[J]. 计算机仿真, 2021, 38(4): 249-253,451.
WANG Shengliang, LIU Genyou. A nonlinear dynamic adaptive inertial weight particle swarm optimization[J]. Computer integrated manufacturing systems, 2021, 38(4): 249-253,451.
[26] 魏德宾, 潘成胜, 杨力, 等. 基于自相似网络流量预测的自适应随机早期检测算法[J/OL]. 通信学报: 1-13[2023-07-05]. http://kns.cnki.net/kcms/detail/11.2102.tn.20230625.1700.010.html.
WEI Debin, PAN Chengsheng, YANG Li, et al. Adaptive random early detection algorithm based on self-similar network traffic prediction[J/OL]. Journal on Communications: 1-13[2023-07-05]. http://kns.cnki.net/kcms/detail/11.2102.tn.20230625.1700.010.html.
[27] 弭娜. SDN控制器放置及扩容性能优化验证平台[D]. 济南: 山东大学, 2021.
MI Na. Validation and verification platform of sdn controllerplacement and incremental capacity optimlization[D]. Jinan: Shandong University, 2021.
[28] Alibaba Cluster Trace Program. Alibaba production cluster data [EB/OL]. [2022-01-01]. https://github.com/alibaba/clusterdata/tree/v2018.
[29] ZHOU Ping, SHEN Ke, KUMAR N, et al. Communication-efficient offloading for mobile-edge computing in 5G heterogeneous networks[J]. IEEE internet of things journal, 2021, 8(13): 10237-10247.
备注/Memo
收稿日期:2023-12-27。
基金项目:国家自然科学基金项目（62271036）；国家重点研发计划科技冬奥重点专项（2021YFF0306303）.
作者简介:方浩添，硕士研究生，主要研究方向为移动边缘计算。E-mail：2108110021005@stu.bucea.edu.cn;田乐，副教授，博士，主要研究方向为计算机网络、无线通信、大数据处理。E-mail：tianle@bucea.edu.cn;郭茂祖，教授，博士生导师，主要研究方向为机器学习、数据挖掘理论与算法、智能建造与智慧城市等人工智能应用、生物信息学。获得省杰出青年科学基金项目（2006年），教育部宝钢优秀教师奖（2015年）等荣誉称号，教育部自然科学奖（2019年）、第十届吴文俊人工智能自然科学奖（2020年）以及其他省部级科技奖4项（2002、2008年）、省部级教学成果奖1项（2018年）。主持、参与过3项自然科学基金委重点项目等，发表学术论文290余篇，授权专利、软件著作权20余项。E-mail：guomaozu@bucea.edu.cn。
通讯作者:郭茂祖. E-mail：guomaozu@bucea.edu.cn
更新日期/Last Update:
2024-11-05