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

doi:10.1007/s12204-020-2159-2

Journal of Shanghai Jiao Tong University (Science) ›› 2020, Vol. 25 ›› Issue (2): 201-207.doi: 10.1007/s12204-020-2159-2

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

WU Xin (武星), PU Juan (蒲娟), XIE Shaorong (谢少荣)

(1. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; 2. Shanghai Institute for Advanced Communication and Data Science, Shanghai 200444, China)

出版日期:2020-04-01 发布日期:2020-04-01
通讯作者: WU Xin (武星) E-mail:xingwu@shu.edu.cn

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

WU Xin (武星), PU Juan (蒲娟), XIE Shaorong (谢少荣)

(1. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; 2. Shanghai Institute for Advanced Communication and Data Science, Shanghai 200444, China)

Online:2020-04-01 Published:2020-04-01
Contact: WU Xin (武星) E-mail:xingwu@shu.edu.cn

摘要/Abstract

摘要： 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 USV 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.

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

Abstract: 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 USV 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)

中图分类号:

TP 399

WU Xin (武星), PU Juan (蒲娟), XIE Shaorong (谢少荣) . Attacking Strategy of Multiple Unmanned Surface Vehicles with Improved GWO Algorithm Under Control of Unmanned Aerial Vehicles[J]. Journal of Shanghai Jiao Tong University (Science), 2020, 25(2): 201-207.

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

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

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