HCN-MOEA: a robustness optimization algorithm for large-scale heterogeneous combat network

Changrong Xie; Hui Li; Yangang Huang; Kebin Chen

doi:10.1117/12.2685061

19 October 2023 HCN-MOEA: a robustness optimization algorithm for large-scale heterogeneous combat network

Changrong Xie, Hui Li, Yangang Huang, Kebin Chen

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127093U (2023) https://doi.org/10.1117/12.2685061
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

The research on robustness optimization of large-scale heterogeneous combat network(HCN) is of great significance to improve the survival ability of combat system in complex battlefield and ensure that it can function even when some of its components fail. Most of the existing optimization algorithms are applied in homogeneous networks, and their application to HCNs invariably leads to information loss. In order to fill this gap, this study presents a robustness optimization algorithm, known as the HCN-MOEA, which takes into account the characteristics of the heterogeneity of HCN by introducing the functional robustness of HCN. What’s more, we design the heterogeneous mutation operator which removes the edges that are impossible in actual network to reduce the search space and improves the convergence speed. Finally, We execute optimization experiment and comparative experiment to verify the effectiveness of the optimization objective and mutation operator of HCN-MOEA, which proves that it is superior to the existing algorithms in the application of large-scale HCN. Additionally, the optimization results obtained by the experiment can also provide support for building a combat network with better combat capability.

Citation Download Citation

Changrong Xie, Hui Li, Yangang Huang, and Kebin Chen "HCN-MOEA: a robustness optimization algorithm for large-scale heterogeneous combat network", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127093U (19 October 2023); https://doi.org/10.1117/12.2685061

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