车联网中基于位置信息映射和相关性评估的进化多任务优化算法
Location Mapping and Correlation Assessment Based Evolutionary Multi-Task Optimization Algorithm in the Internet of Vehicles
- 电子学报 2025年53卷第5期 页码：1661-1676
- 作者机构：
1.重庆大学计算机学院，重庆 400044
2.重庆赛力斯凤凰智创有限公司，重庆 400000
- 作者简介：
[ "沈俊杰 男，2002年3月出生于四川省达州市.现为重庆大学计算机学院硕士研究生.主要研究方向为车联网、边缘计算、人工智能. E-mail: shenjunjie@stu.cqu.edu.cn" ]
[ "彭江 男，1989年2月出生于湖北省恩施市.2016年硕士毕业于重庆理工大学车辆工程，现为重庆大学2024届博士研究生，赛力斯科技公司智能测试中心副总经理.主要研究方向为智能汽车智能化.E-mail: jiang.peng@seres.cn" ]
[ "郭坤银 男，1982年7月出生于重庆市.现为重庆大学国家卓越工程师学院教师.主要研究方向为服务计算、创业教育. E-mail: css@cqu.edu.cn" ]
[ "刘凯 男，1984年8月出生于四川省成都市.现为重庆大学计算机学院、重庆大学国家卓越工程师学院教授，博士生导师.主要研究方向为车联网、自动驾驶、边缘智能等. E-mail: liukai0807@cqu.edu.cn" ]
- 基金信息：国家自然科学基金(62472055;62172064)
DOI：10.12263/DZXB.20250053
中图分类号： TP393;收稿：2025-01-14，
修回：2025-05-06，
纸质出版：2025-05-25
- 稿件说明：
移动端阅览
沈俊杰, 彭江, 郭坤银, 等. 车联网中基于位置信息映射和相关性评估的进化多任务优化算法[J]. 电子学报, 2025, 53(05): 1661-1676.
SHEN Jun-jie, PENG Jiang, GUO Kun-yin, et al. Location Mapping and Correlation Assessment Based Evolutionary Multi-Task Optimization Algorithm in the Internet of Vehicles[J]. Acta Electronica Sinica, 2025, 53(05): 1661-1676.
沈俊杰, 彭江, 郭坤银, 等. 车联网中基于位置信息映射和相关性评估的进化多任务优化算法[J]. 电子学报, 2025, 53(05): 1661-1676. DOI：10.12263/DZXB.20250053
SHEN Jun-jie, PENG Jiang, GUO Kun-yin, et al. Location Mapping and Correlation Assessment Based Evolutionary Multi-Task Optimization Algorithm in the Internet of Vehicles[J]. Acta Electronica Sinica, 2025, 53(05): 1661-1676. DOI：10.12263/DZXB.20250053
摘要
随着车联网（Internet of Vehicles，IoV）和智能交通系统的兴起，计算成本的增加和问题规模的扩大使得实时应用的实现变得极具挑战性，同时也为车载边缘计算（Vehicular Edge Computing，VEC）带来了大量亟待并行求解的组合优化问题.这些复杂的实际问题往往具有非凸性、不可微性，甚至存在黑盒目标与约束条件，可能会超出传统数学方法的解决范围.进化多任务优化（Evolutionary Multi-Task Optimization，EMTO）作为一种新兴的多任务优化范式，通过充分利用任务间的潜在相关性，能够有效地实现多个独立优化任务的并行求解.本文设计了一种IoV显式EMTO框架，结合IoV任务的特点，深入挖掘任务间隐含的关联性，并提出了一种基于车辆位置映射和相关性评估的IoV EMTO算法.针对IoV环境下的多任务优化问题，本文对车-路数据路由（Data Routing，DR）、车-路服务迁移（Service Migration，SM）、车-车消息传输（Message Transmission，MT）和车-车任务卸载（Task Offloading，TO）四个问题进行联合优化，目标是在限定时间内最大化各个任务的交付率.进一步地，为了在任务相关性未知的情况下提升相关任务间的知识迁移效率，本文在算法中设计并引入了基于任务相关性评估的迁移机制.具体而言，通过计算链路间最长公共子序列来计算链路的相似度，针对不同的相关性分布情况设计了三种迁移策略，以确保算法在不同场景下的知识迁移能力.最后，本文通过实验验证和性能评估，验证了所提框架和算法的有效性，与其他的EMTO算法相比，本文所提算法在各优化问题上的收敛速度更快，种群间知识迁移后的求解效果更好，展现出良好的性能.
Abstract
With the rise of the internet of vehicles (IoV) and intelligent transportation systems
the increasing computational costs and problem scale have made the implementation of real-time applications extremely challenging
while also bringing a large number of combinatorial optimization problems that are in urgent need of parallel solving to vehicular edge computing (VEC). Often
these complex practical problems may possess non-convex
non-differentiable or even black-box objectives and constraints
which may go beyond the scope that traditional mathematical methods can handle. In this context
evolutionary multi-task optimization (EMTO)
as a new paradigm in the field of multi-task optimization
effectively solves multiple independent optimization tasks in parallel by fully exploiting the potential correlations between tasks. An explicit EMTO framework tailored for IoV is designed. By integrating the unique characteristics of IoV tasks and deeply exploring the implicit correlations among them
a novel EMTO approach for IoV is proposed
which establishes mappings based on vehicle location information. This paper focuses on the multi-task optimization problem in the context of IoV
jointly optimizing fouraspects: vehicle-to-road data routing (DR)
vehicle-to-road service migration (SM)
vehicle-to-vehicle message transmission (MT)
and vehicle-to-vehicle task offloading (TO)
with the objective of maximizing the delivery rate of each task within a specified time frame. Furthermore
to enhance the efficiency of knowledge transfer among related tasks when their correlations are unknown
a migration mechanism grounded in task correlation assessment is introduced. Specifically
the longest common subsequence between links is utilized to calculate their similarity
and three migration strategies are devised according to different correlation distributions
ensuring the algorithm’s capability of knowledge transfer across various scenarios. Finally
through experimental validation and performance evaluation
the effectiveness of the proposed framework and algorithm is demonstrated. Compared with other EMTO algorithms
the algorithm presented in this paper exhibits faster convergence speeds for various optimization problems and yields better solutions after knowledge transfer among populations
showcasing impressive results.
关键词
Keywords
references
江恺 , 曹越 , 周欢 , 等 . 车联网边缘智能: 概念、架构、问题、实施和展望 [J ] . 物联网学报 , 2023 , 7 ( 1 ): 37 - 48 .
JIANG K , CAO Y , ZHOU H , et al . Edge intelligence empowered internet of vehicles: Concept, framework, issues, implementation, and prospect [J ] . Chinese Journal on Internet of Things , 2023 , 7 ( 1 ): 37 - 48 . (in Chinese)
XU X L , YANG C Y , BILAL M , et al . Computation offloading for energy and delay trade-offs with traffic flow prediction in edge computing-enabled IoV [J ] . IEEE Transactions on Intelligent Transportation Systems , 2023 , 24 ( 12 ): 15613 - 15623 .
FAN W H , SU Y , LIU J , et al . Joint task offloading and resource allocation for vehicular edge computing based on V2I and V2V modes [J ] . IEEE Transactions on Intelligent Transportation Systems , 2023 , 24 ( 4 ): 4277 - 4292 .
LIU K , LIU C H , YAN G Z , et al . Accelerating DNN inference with reliability guarantee in vehicular edge computing [J ] . IEEE/ACM Transactions on Networking , 2023 , 31 ( 6 ): 3238 - 3253 .
YAN G Z , LIU K , LIU C H , et al . Edge intelligence for Internet of vehicles: A survey [J ] . IEEE Transactions on Consumer Electronics , 2024 , 70 ( 2 ): 4858 - 4877 .
许小龙 , 杨威 , 杨辰翊 , 等 . 车联网边缘计算环境下基于流量预测的高效任务卸载策略研究 [J ] . 电子学报 , 2025 , 53 ( 2 ): 329 - 343 .
XU X L , YANG W , YANG C Y , et al . Research on efficient task unloading strategy based on traffic prediction in edge computing environment of internet of vehicles [J ] . Aata Electronica Sinica , 2025 , 53 ( 2 ): 329 - 343 . (in Chinese)
REN H L , LIU K , YAN G Z , et al . Truthful auction mechanisms for dependent task offloading in vehicular edge computing [J ] . IEEE Transactions on Mobile Computing , 2024 , 23 ( 12 ): 14987 - 15002 .
李可 , 马赛 , 戴朋林 , 等 . 基于多目标深度强化学习的车车通信无线资源分配算法 [J ] . 计算机研究与发展 , 2024 , 61 ( 9 ): 2229 - 2245 .
LI K , MA S , DAI P L , et al . Wireless resource allocation algorithm based on multi-objective deep reinforcement learning for vehicle-to-vehicle communications [J ] . Journal of Computer Research and Development , 2024 , 61 ( 9 ): 2229 - 2245 . (in Chinese)
ZHAO H , NING X H , LIU X T , et al . What makes evolutionary multi-task optimization better: A comprehensive survey [J ] . Applied Soft Computing , 2023 , 145 : 110545 .
YAO L , XU X L , BILAL M , et al . Dynamic edge computation offloading for Internet of vehicles with deep reinforcement learning [J ] . IEEE Transactions on Intelligent Transportation Systems , 2023 , 24 ( 11 ): 12991 - 12999 .
HUI Y L , WANG Q Q , SU Z , et al . Joint optimization of personalized route planning and global traffic scheduling: A DT-enabled integrated framework [J ] . IEEE Transactions on Vehicular Technology , 2024 , 73 ( 10 ): 14474 - 14485 .
WU Q , WANG W H , FAN P Y , et al . URLLC-awared resource allocation for heterogeneous vehicular edge computing [J ] . IEEE Transactions on Vehicular Technology , 2024 , 73 ( 8 ): 11789 - 11805 .
ZHANG R , WU L B , CAO S Q , et al . A vehicular task offloading method with eliminating redundant tasks in 5G HetNets [J ] . IEEE Transactions on Network and Service Management , 2023 , 20 ( 1 ): 456 - 470 .
朱思峰 , 蔡江昊 , 柴争义 , 等 . 车联网云边协同计算场景下的多目标优化卸载决策 [J ] . 通信学报 , 2022 , 43 ( 6 ): 223 - 234 .
ZHU S F , CAI J H , CHAI Z Y , et al . Multi-objective optimal offloading decision for cloud-edge collaborative computing scenario in Internet of vehicles [J ] . Journal on Communications , 2022 , 43 ( 6 ): 223 - 234 . (in Chinese)
GUPTA A , ONG Y S , FENG L , et al . Multiobjective multifactorial optimization in evolutionary multitasking [J ] . IEEE Transactions on Cybernetics , 2017 , 47 ( 7 ): 1652 - 1665 .
ZHOU L , FENG L , ZHONG J H , et al . A study of similarity measure between tasks for multifactorial evolutionary algorithm [C ] // Proceedings of the Genetic and Evolutionary Computation Conference Companion . New York : ACM , 2018 : 229 - 230 .
BALI K K , ONG Y S , GUPTA A , et al . Multifactorial evolutionary algorithm with online transfer parameter estimation: MFEA-II [J ] . IEEE Transactions on Evolutionary Computation , 2020 , 24 ( 1 ): 69 - 83 .
YI J , ZHANG W , BAI J R , et al . Multifactorial evolutionary algorithm based on improved dynamical decomposition for many-objective optimization problems [J ] . IEEE Transactions on Evolutionary Computation , 2022 , 26 ( 2 ): 334 - 348 .
WANG B C , LIU Z Z , SONG W . Solving constrained optimization problems via multifactorial evolution [J ] . Applied Soft Computing , 2022 , 127 : 109392 .
THANG T B , BINH H T T . A hybrid multifactorial evolutionary algorithm and firefly algorithm for the clustered minimum routing cost tree problem [J ] . Knowledge-Based Systems , 2022 , 241 : 108225 .
FENG Y L , FENG L , KWONG S , et al . A multivariation multifactorial evolutionary algorithm for large-scale multiobjective optimization [J ] . IEEE Transactions on Evolutionary Computation , 2022 , 26 ( 2 ): 248 - 262 .
LIU S B , LI J , LIN Q Z , et al . Evolutionary large-scale multiobjective optimization via autoencoder-based problem transformation [J ] . IEEE Transactions on Emerging Topics in Computational Intelligence , 2024 , 8 ( 4 ): 2709 - 2722 .
FENG L , ZHOU L , ZHONG J H , et al . Evolutionary multitasking via explicit autoencoding [J ] . IEEE Transactions on Cybernetics , 2019 , 49 ( 9 ): 3457 - 3470 .
FENG L , HUANG Y X , ZHOU L , et al . Explicit evolutionary multitasking for combinatorial optimization: A case study on capacitated vehicle routing problem [J ] . IEEE Transactions on Cybernetics , 2021 , 51 ( 6 ): 3143 - 3156 .
TANG Z D , GONG M G , WU Y , et al . Regularized evolutionary multitask optimization: Learning to intertask transfer in aligned subspace [J ] . IEEE Transactions on Evolutionary Computation , 2021 , 25 ( 2 ): 262 - 276 .
GAO K L , YANG C E , DING J L , et al . Distributed knowledge transfer for evolutionary multitask multimodal optimization [J ] . IEEE Transactions on Evolutionary Computation , 2024 , 28 ( 4 ): 1141 - 1155 .
LI Y C , GONG W Y , LI S J . Multitasking optimization via an adaptive solver multitasking evolutionary framework [J ] . Information Sciences , 2023 , 630 : 688 - 712 .
ZHOU J J , RAO S J , GAO L , et al . Solving many-task optimization problems via online intertask learning [J ] . Expert Systems with Applications , 2023 , 225 : 120110 .
ZHOU X , WANG Z K , FENG L , et al . Toward evolutionary multitask convolutional neural architecture search [J ] . IEEE Transactions on Evolutionary Computation , 2024 , 28 ( 3 ): 682 - 695 .
MA Z T , ZHONG J H , LIU W L , et al . Accelerating evolutionary multitasking optimization with a generalized GPU-based framework [J ] . IEEE Transactions on Emerging Topics in Computational Intelligence , 2024 , 8 ( 6 ): 3995 - 4010 .
LI J Q , CAI J C , SUN T , et al . Multitask-based evolutionary optimization for vehicle routing problems in autonomous transportation [J ] . IEEE Transactions on Automation Science and Engineering , 2024 , 21 ( 3 ): 2400 - 2411 .
YI J , BAI J R , HE H B , et al . A multifactorial evolutionary algorithm for multitasking under interval uncertainties [J ] . IEEE Transactions on Evolutionary Computation , 2020 , 24 ( 5 ): 908 - 922 .
GUPTA A , ZHOU L , ONG Y S , et al . Half a dozen real-world applications of evolutionary multitasking, and more [J ] . IEEE Computational Intelligence Magazine , 2022 , 17 ( 2 ): 49 - 66 .
LI W C . Multi-receiver data authorization with data search for data sharing in cloud-assisted IoV [J ] . IEEE Transactions on Intelligent Transportation Systems , 2024 , 25 ( 5 ): 4233 - 4250 .
FAN Q B , CHEN L , YOU C S , et al . Dependency-aware service migration for backhaul-free vehicular edge computing networks [J ] . IEEE Transactions on Vehicular Technology , 2024 , 73 ( 1 ): 1337 - 1352 .
CAO B Q , YE H F , LIU J X , et al . SMART: Cost-aware service migration path selection based on deep reinforcement learning [J ] . IEEE Transactions on Intelligent Transportation Systems , 2024 , 25 ( 9 ): 12421 - 12436 .
LIU C H , LIU K . Toward reliable DNN-based task partitioning and offloading in vehicular edge computing [J ] . IEEE Transactions on Consumer Electronics , 2024 , 70 ( 1 ): 3349 - 3360 .
ZHOU Y K , REN H L , XIAO K , et al . Joint Data Routing and Service Migration via Evolutionary Multitasking Optimization in Vehicular Networks [M ] // International Conference on Neural Computing for Advanced Applications . Singapore : Springer Nature Singapore , 2023 : 434 - 449 .
0
浏览量
13
下载量
0
CSCD
- 网络PDF下载
- Meta-XML下载
- BibTeX下载
- Endnote下载
- RefMan 下载
- NoteExpress下载
- NoteFirst下载
关联资源
相关文章
相关作者
相关机构
- 技术支持由北京北大方正电子有限公司提供 京ICP备09064830号-19
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies