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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Veröffentlicht in:	Cluster computing Jg. 28; H. 6; S. 397
Hauptverfasser:	Xiong, Hui, Yao, Chenyang, Liu, Jinzhen, Shi, Xiuzhi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.10.2025 Springer Nature B.V
Schlagworte:	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
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Abstract	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.
AbstractList	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.
ArticleNumber	397
Author	Yao, Chenyang Xiong, Hui Liu, Jinzhen Shi, Xiuzhi
Author_xml	– sequence: 1 givenname: Hui surname: Xiong fullname: Xiong, Hui email: xionghui@tiangong.edu.cn organization: School of Control Science and Engineering, Tiangong University, Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University – sequence: 2 givenname: Chenyang surname: Yao fullname: Yao, Chenyang organization: School of Artificial Intelligence, Tiangong University, Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University – sequence: 3 givenname: Jinzhen surname: Liu fullname: Liu, Jinzhen organization: School of Control Science and Engineering, Tiangong University, Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University – sequence: 4 givenname: Xiuzhi surname: Shi fullname: Shi, Xiuzhi organization: School of Control Science and Engineering, Tiangong University, Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University
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SubjectTerms	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
Title	Improved multi-objective gray wolf optimization for task allocation in multi-UAV heterogeneous targets reconnaissance
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