Location Mapping and Correlation Assessment Based Evolutionary Multi-Task Optimization Algorithm in the Internet of Vehicles

SHEN Jun-jie; PENG Jiang; GUO Kun-yin; LIU Kai

doi:10.12263/DZXB.20250053

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Location Mapping and Correlation Assessment Based Evolutionary Multi-Task Optimization Algorithm in the Internet of Vehicles

PAPERS | 更新时间：2025-08-18

- Location Mapping and Correlation Assessment Based Evolutionary Multi-Task Optimization Algorithm in the Internet of Vehicles
- ACTA ELECTRONICA SINICA Vol. 53, Issue 5, Pages: 1661-1676(2025)
- 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：
- 基金信息：
  
  National Natural Science Foundation of China(62472055;62172064)
- DOI：10.12263/DZXB.20250053
  CLC： TP393;
- Received：14 January 2025，
  
  Revised：2025-05-06，
  
  Published：25 May 2025
- 稿件说明：
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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

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