Journal of System Simulation
Abstract
Abstract: The training effect in the training simulation scenario is not ideal. Therefore, in order to obtain the training simulation scenario with a better training effect, the training simulation scenario is optimized, and a training simulation scenario generation method based on the PSO algorithm is proposed. A fitness function is constructed based on the improved situation assessment method of the power field model, and the ability weight parameters are determined by combining the improved AHP with computer simulation software; by instantiating particles with the attributes of the combat platform, the particle swarm optimization algorithm is improved to solve the optimization scheme of training simulation scenarios. The method is validated using computer simulation cases, and the training simulation scenario results before and after optimization are compared on a computer simulation platform. The results show that this method can adjust the difficulty of training simulation scenarios, effectively help optimize the generation of training simulation scenarios, and solve the optimization generation problem of training simulation scenarios.
Recommended Citation
Gong, Jianxing; Wang, Zimu; and Yang, Qilong
(2023)
"Training Simulation Scenario Generation Based on Particle Swarm Optimization,"
Journal of System Simulation: Vol. 35:
Iss.
9, Article 4.
DOI: 10.16182/j.issn1004731x.joss.23-0581
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol35/iss9/4
First Page
1860
Last Page
1870
CLC
TP391
Recommended Citation
Gong Jianxing, Wang Zimu, Yang Qilong. Training Simulation Scenario Generation Based on Particle Swarm Optimization[J]. Journal of System Simulation, 2023, 35(9): 1860-1870.
DOI
10.16182/j.issn1004731x.joss.23-0581
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