TY - GEN
T1 - A Research on Kill Net Generation Technology of Cooperative Combat System Based on SG-DPSO Algorithm
AU - Wu, Shuying
AU - Kang, Peiqi
AU - Li, Jiarui
AU - Li, Bo
N1 - Publisher Copyright:
© Beijing HIWING Scientific and Technological Information Institute 2025.
PY - 2025
Y1 - 2025
N2 - In the process of System-of-Systems, the coordinated kill net composed of platforms has a strong adaptive ability to dynamically generated threats and changing environments, which provides the basic support for cross-domain cooperative operations. The modern battlefield environment is full of uncertainty, so it is very important for military command decision to build kill net based on operational demand and current situation of battlefield. In this paper, an optimization algorithm is proposed based on discrete particle swarm optimization, aiming at the problem of kill net generation under the constraint of the topological relationship of the combat network, which can pre-plan before the battle. The population renewal method based on cross variation is constructed in discrete domain, and the self-organizing inertia weight and acceleration coefficient are integrated. All these methods improve the efficiency of both global search and local search, and overcomes the problem of particle swarm algorithm easily falling into local optimal solutions.
AB - In the process of System-of-Systems, the coordinated kill net composed of platforms has a strong adaptive ability to dynamically generated threats and changing environments, which provides the basic support for cross-domain cooperative operations. The modern battlefield environment is full of uncertainty, so it is very important for military command decision to build kill net based on operational demand and current situation of battlefield. In this paper, an optimization algorithm is proposed based on discrete particle swarm optimization, aiming at the problem of kill net generation under the constraint of the topological relationship of the combat network, which can pre-plan before the battle. The population renewal method based on cross variation is constructed in discrete domain, and the self-organizing inertia weight and acceleration coefficient are integrated. All these methods improve the efficiency of both global search and local search, and overcomes the problem of particle swarm algorithm easily falling into local optimal solutions.
KW - Kill Web
KW - Multi-objective Optimization
KW - System of Systems
UR - http://www.scopus.com/inward/record.url?scp=105002468741&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-3592-4_14
DO - 10.1007/978-981-96-3592-4_14
M3 - 会议稿件
AN - SCOPUS:105002468741
SN - 9789819635917
T3 - Lecture Notes in Electrical Engineering
SP - 128
EP - 138
BT - Proceedings of 4th 2024 International Conference on Autonomous Unmanned Systems, 4th ICAUS 2024 - Volume VII
A2 - Liu, Lianqing
A2 - Niu, Yifeng
A2 - Fu, Wenxing
A2 - Qu, Yi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Autonomous Unmanned Systems, ICAUS 2024
Y2 - 19 September 2024 through 21 September 2024
ER -