Research on Unmanned Swarm Combat System Adaptive Evolution Model Simulation

doi:10.16182/j.issn1004731x.joss.22-FZ0893

Abstract

Abstract:

Aiming at the fact that the intelligent unmanned swarm combat system is mainly composed of large-scale combat individuals with limited behavioral capabilities and has limited ability to adapt to the changes of battlefield environment and combat opponents, a learning evolution method combining genetic algorithm and reinforcement learning is proposed to construct an individual-based unmanned bee colony combat system evolution model. To improve the adaptive evolution efficiency of bee colony combat system, an improved genetic algorithm is proposed to improve the learning and evolution speed of bee colony individuals by using individual-specific mutation optimization strategy. Simulation experiment on SWARM platform of complex system modeling and simulation verify the effectiveness of the proposed theoretical method.

Key words: unmanned swarm, genetic algorithms, adaptability, evolution, reinforcement learning

CLC Number:

TP949

Zhiqiang Li, Yuanlong Li, Laixiang Yin, Xiangping Ma. Research on Unmanned Swarm Combat System Adaptive Evolution Model Simulation[J]. Journal of System Simulation, 2023, 35(4): 878-886.

Figures/Tables 12

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