Improved multi-objective gray wolf optimization for task allocation in multi-UAV heterogeneous targets reconnaissance

In recent years, unmanned aerial vehicles (UAVs) reconnaissance task allocation are attracting more and more research attention. The efficient allocation of UAV resources is a fundamental and challenging problem. In this paper, the heterogeneous reconnaissance targets are categorized into point, lin...

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Bibliographic Details
Published in:Cluster computing Vol. 28; no. 6; p. 397
Main Authors: Xiong, Hui, Yao, Chenyang, Liu, Jinzhen, Shi, Xiuzhi
Format: Journal Article
Language:English
Published: New York Springer US 01.10.2025
Springer Nature B.V
Subjects:
Assignment problem
Collaboration
Computer Communication Networks
Computer Science
Decision making
Efficiency
Evolutionary algorithms
Genetic algorithms
Multiple objective analysis
Mutation
Operating Systems
Optimization
Optimization models
Pareto optimization
Processor Architectures
Reconnaissance aircraft
Sensors
Traveling salesman problem
Unmanned aerial vehicles
Reconnaissance task allocation
Unmanned aerial vehicles
Multi-objective optimization
Gray wolf optimization algorithm
Dubins vehicles
ISSN:1386-7857, 1573-7543
Online Access:Get full text
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Summary:In recent years, unmanned aerial vehicles (UAVs) reconnaissance task allocation are attracting more and more research attention. The efficient allocation of UAV resources is a fundamental and challenging problem. In this paper, the heterogeneous reconnaissance targets are categorized into point, line, and area targets. Considering the constraints of UAV flight path and remaining resources, we construct a multi-objective optimization model with fuel cost and total task time cost as the optimization objectives. To solve this model, an improved multi-objective gray wolf optimization (IMOGWO) algorithm is proposed, which employs three novel improved strategies to balance the exploration and exploitation abilities. Firstly, a nonlinear convergence factor is designed to strengthen the global search ability of the algorithm. Secondly, an evolutionary strategy is introduced to improve the population diversity to help the population jumps out of the local optimum. Finally, a Pareto front optimization strategy is adopted to remove the sub equivalent solutions and maintain the Pareto front set. Compared with the popular and classic multi-objective algorithms, the simulation results verify the effectiveness and superiority of the IMOGWO algorithm in solving the task allocation problem. Furthermore, its superiority becomes more pronounced as the problem scale increases.
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ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-024-05048-4