Dynamic Multi-objective AWPSO in DT-Assisted UAV Cooperative Task Assignment

In recent years, more and more attention has been paid to the unmanned aerial vehicle (UAV) cooperative task assignment. In order to complete the task with the lowest cost, some researchers use multi-objective optimization to solve the assignment problem. But few of them consider the complex dynamic...

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Bibliographic Details
Published in:IEEE journal on selected areas in communications Vol. 41; no. 11; p. 1
Main Authors: Deng, Min, Yao, Zhiqiang, Li, Xingwang, Wang, Han, Nallanathan, Arumugam, Zhang, Zeyang
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Assignment problem
Autonomous aerial vehicles
Change detection algorithms
Costs
digital twin
Digital twins
dynamic multi-objective optimization
evolutionary algorithm
Heuristic algorithms
Heuristic methods
Jamming
Multiple objective analysis
Operations research
Optimization
Particle swarm optimization
Subgroups
Task analysis
task assignment
Task scheduling
UAV
Unmanned aerial vehicles
Weapons
ISSN:0733-8716, 1558-0008
Online Access:Get full text
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Summary:In recent years, more and more attention has been paid to the unmanned aerial vehicle (UAV) cooperative task assignment. In order to complete the task with the lowest cost, some researchers use multi-objective optimization to solve the assignment problem. But few of them consider the complex dynamic scenarios. In this article, the time-varying resource supply and demands are provided by established digital twins (DTs) of UAVs and targets, thereby enabling accurate decision guidance for dynamic task assignment. It takes the scheduling cost, path cost, risk cost and total task time cost as the optimization objectives. To solve this model, an improved dynamic multi-objective adaptive weighted particle swarm Optimization algorithm (DMOAWPSO) is proposed. In the initialization stage, a heuristic method is used to increase the effectiveness of the solution. Besides, the adaptive mutation and subgroup methods are adopted to improve the diversity of the solution. Then, effective environment change detection and response strategies are designed to adapt to dynamic scenarios. Finally, the evaluation metrics are calculated in different instances. Compared with the popular and classic dynamic multi-objective algorithms, the simulation results verify that the proposed algorithm is effective and can cope with the environment changes better in solving the task assignment problem.
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ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2023.3310056