Deep Reinforcement Learning; Internet of Vehicles; Mobile Edge Computing; Multi-objective Optimization; Aerial vehicle; Dynamic multiobjective optimization; Edge computing; Energy-consumption; Internet of vehicle; Multi objective; Multi-objectives optimization; Overall networks; Performance; Reinforcement learnings; Automotive Engineering; Aerospace Engineering; Computer Networks and Communications; Electrical and Electronic Engineering
Abstract :
[en] Dynamic multi-objective optimization in Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) for Internet of Vehicles (IoV) faces significant challenges, due to complex operational environments and conflicting objectives. While Deep Reinforcement Learning (DRL) enables real-time optimization, conventional weighted-sum approaches fail to balance these objectives effectively. To address this, we propose a Multi-Objective Decomposition Evolutionary DRL (MODE-DRL) framework, which include the following three innovative aspects. Firstly, a multi-objective optimization model is developed, aiming to minimize delay and energy consumption while maximizing the number of completed tasks, thus ensuring overall network performance. Secondly, a novel MODE strategy that dynamically associates weight vectors with learning agents to optimize policy distribution and enhance population diversity. Lastly, two integrated algorithms, called MODE with Proximal Policy Optimization (MODE-PPO) and MODE with Deep Deterministic Policy Gradient (MODE-DDPG), are developed to combine DRL's dynamic decision-making with MODE's global optimization capabilities, enabling agents to rapidly adapt strategies based on different weights. Experimental results demonstrate that the MODE-DRL achieves a 33.2% improvement in hypervolume, along with a 16.3% reduction in average delay, a 15.5% decrease in average energy consumption, and a 34.4% increase in average number of completed tasks. These results confirm that MODE-DRL exhibits significant advantages in both convergence and diversity, while enhancing overall network performance. This work provides a scalable paradigm for real-time multi-objective decision-making in UAV-assisted MEC for IoV systems.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Zhang, Lei; China Three Gorges University, Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, Yichang, China ; China Three Gorges University, College of Computer and Information Technology, Yichang, China
Tian, Can; China Three Gorges University, Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, Yichang, China ; China Three Gorges University, College of Computer and Information Technology, Yichang, China
Liu, Tingting; China Three Gorges University, School of Economics and Management, Yichang, China
Li, Xingwang; Henan Polytechnic University, School of Physics and Electronics Information Engineering, Jiaozuo, China
Mumtaz, Shahid; Department of Applied Informatics Silesian University of Technology, Gliwice, Poland ; Nottingham Trent University, Department of Engineering, Nottingham, United Kingdom
KHAN, Wali Ullah ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
External co-authors :
yes
Language :
English
Title :
Multi-Objective Decomposition Evolutionary DRL for UAV-Assisted MEC in Internet of Vehicles
Alternative titles :
[en] Multi-Objective Decomposition Evolutionary DRL for UAV-Assisted MEC in Internet of Vehicles
Original title :
[en] Multi-Objective Decomposition Evolutionary DRL for UAV-Assisted MEC in Internet of Vehicles
Publication date :
26 August 2025
Journal title :
IEEE Transactions on Vehicular Technology
ISSN :
0018-9545
Publisher :
Institute of Electrical and Electronics Engineers Inc.
This work was supported in part by the National Natural Science Foundation of China under Grant Number 62271286, 62371271, 42406173, in part by the Open Fund From Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering under Grant Number 2024SDSJ02. (Corresponding author: Tingting Liu) Lei Zhang and Can Tian are with Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443005, China; College of Computer and Information Technology, China Three Gorges University, Yichang 443005, China (e-mail: zhanglei@ctgu.edu.cn, tic@ctgu.edu.cn).
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