Attacking Strategy of Multiple Unmanned Surface Vehicles with Improved GWO Algorithm Under Control of Unmanned Aerial Vehicles

  • Xin Wu (武 星)Email author
  • Juan Pu (蒲 娟)
  • Shaorong Xie (谢少荣)


Unmanned combat system is one of the important means to capture information superiority, carry out precision strike and accomplish special combat tasks in information war. Unmanned attack strategy plays a crucial role in unmanned combat system, which has to ensure the attack by unmanned surface vehicles (USVs) from failure. To meet the challenge, we propose a task allocation algorithm called distributed auction mechanism task allocation with grey wolf optimization (DAGWO). The traditional grey wolf optimization (GWO) algorithm is improved with a distributed auction mechanism (DAM) to constrain the initialization of wolves, which improves the optimization process according to the actual situation. In addition, one unmanned aerial vehicle (UAV) is employed as the central control system to establish task allocation model and construct fitness function for the multiple constraints of US V attack problem. The proposed DAGWO algorithm can not only ensure the diversity of wolves, but also avoid the local optimum problem. Simulation results show that the proposed DAGWO algorithm can effectively solve the problem of attack task allocation among multiple USVs.

Key words

unmanned surface vehicle (USV) attack strategy grey wolf optimization (GWO) task allocation unmanned aerial vehicle (UAV) 

CLC number

TP 399 

Document code


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Copyright information

© Shanghai Jiao Tong University and Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  • Xin Wu (武 星)
    • 1
    • 2
    Email author
  • Juan Pu (蒲 娟)
    • 1
  • Shaorong Xie (谢少荣)
    • 1
  1. 1.School of Computer Engineering and ScienceShanghai UniversityShanghaiChina
  2. 2.Shanghai Institute for Advanced Communication and Data ScienceShanghaiChina

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