TY - GEN
T1 - Target Selection for Multi-domain Combat SoS Breaking with Operational Constraints
AU - Ma, Chaoxiong
AU - Liang, Yan
AU - Xu, Hongfeng
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Modern warfare has become increasingly expensive as a result of its multi-domain joint systematic combat model. To achieve the purpose of stopping war with war and maintaining the safety of people's lives and property, paralyzing the target system of systems (SoS) operational capability maximally under constraints, such as battlefield resources and environment, has become an urgent research topic. This paper addresses the problem of fast search for the optimal solution of the strike target selection task, which considers operational constraints, dynamic coupling of node states, and dimensional explosion. Firstly, a capability aggregation-based target SoS operational capability calculation model and an operational constraint-based strike target selection model are established. Then, an adaptive genetic simulated annealing algorithm (AGSA) is proposed for fast model solving. The algorithm is carefully designed to address the constraints of the strike target selection task, which enables it to accomplish individual selection and coding updates, improve population diversity, and ensure search performance. Eventually, the simulation results under three different scenarios demonstrate the effectiveness of AGSA.
AB - Modern warfare has become increasingly expensive as a result of its multi-domain joint systematic combat model. To achieve the purpose of stopping war with war and maintaining the safety of people's lives and property, paralyzing the target system of systems (SoS) operational capability maximally under constraints, such as battlefield resources and environment, has become an urgent research topic. This paper addresses the problem of fast search for the optimal solution of the strike target selection task, which considers operational constraints, dynamic coupling of node states, and dimensional explosion. Firstly, a capability aggregation-based target SoS operational capability calculation model and an operational constraint-based strike target selection model are established. Then, an adaptive genetic simulated annealing algorithm (AGSA) is proposed for fast model solving. The algorithm is carefully designed to address the constraints of the strike target selection task, which enables it to accomplish individual selection and coding updates, improve population diversity, and ensure search performance. Eventually, the simulation results under three different scenarios demonstrate the effectiveness of AGSA.
UR - http://www.scopus.com/inward/record.url?scp=85171568384&partnerID=8YFLogxK
U2 - 10.1109/ICARM58088.2023.10218837
DO - 10.1109/ICARM58088.2023.10218837
M3 - 会议稿件
AN - SCOPUS:85171568384
T3 - 2023 8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023
SP - 297
EP - 302
BT - 2023 8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023
Y2 - 8 July 2023 through 10 July 2023
ER -