Journal of System Simulation
Abstract
Abstract: Under the research background of collaborative multi-aircraft and multi-target air combats, combined with the actual combat constraint conditions and the threat assessment functions on both sides, a collaborative air combat target decision simulation model is established for complex and changeable battlefield situations, which can reflect the priority of fire attack. To solve the decision scheme quickly and accurately, an improved multi-agent particle swarm optimization algorithm is proposed by introducing the interaction mechanism of the multi-agent theory into particle swarm optimization algorithm; and the neighborhood cooperation operator, mutation operator and self-learning operator for the agent are designed respectively. The simulation results show that the method can work out a reasonable and effective decision scheme, and has a good real-time simulating performance.
Recommended Citation
Fu, Yuewen; Wang, Yuancheng; Zhen, Chen; and Fan, Wenlan
(2019)
"Target Decision in Collaborative Air Combats Using Multi-agent Particle Swarm Optimization,"
Journal of System Simulation: Vol. 30:
Iss.
11, Article 13.
DOI: 10.16182/j.issn1004731x.joss.201811013
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol30/iss11/13
First Page
4151
Revised Date
2018-07-12
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.201811013
Last Page
4157
CLC
TP391.9
Recommended Citation
Fu Yuewen, Wang Yuancheng, Chen Zhen, Fan Wenlan. Target Decision in Collaborative Air Combats Using Multi-agent Particle Swarm Optimization[J]. Journal of System Simulation, 2018, 30(11): 4151-4157.
DOI
10.16182/j.issn1004731x.joss.201811013
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