基于优化XGBoost的无人机集群作战效能评估及分析

doi:10.6040/j.issn.1672-3961.0.2025.102

摘要/Abstract

摘要： 针对无人机集群作战过程的高时效性、行为复杂性、信息数据庞大等特点在作战效能评估方面产生的巨大挑战,提出一种基于粒子群优化(particle swarm optimization, PSO)-极端梯度提升(extreme gradient boosting, XGBoost)-沙普利加性解释(Shapley additive explanations, SHAP)模型的数据驱动效能评估方法。由系统评估指标出发,采用XGBoost算法建立效能评估模型,基于仿真数据分析验证指标数据与作战效能间的映射关系,挖掘无人机集群体系作战效能;利用PSO算法优化XGBoost模型超参数,提高评估精度与效率;为兼顾评估工作的预测性与可解释性,采用SHAP机制对效能评估过程进行解释,给出指标优化改进方向。通过Vensim平台采集的无人机集群作战仿真数据进行验证,相比支持向量回归(support vector regression, SVR)、随机森林(random forest, RF)算法、轻量级梯度提升机(light gradient boosting machine, LightGBM)算法及反向传播(back propagation, BP)神经网络方法,所提模型具有更好的评估精度及可解释性。

关键词: 无人机集群, 作战效能评估, 机器学习, XGBoost, SHAP

Abstract: To address the huge challenges of high time-sensitivity, behavioral complexity, and massive data volumes in the combat effectiveness evaluation of unmanned aerial vehicle(UAV)cluster, a data-driven effectiveness evaluation method based on the particle swarm optimization(PSO)-extreme gradient boosting(XGBoost)-Shapley additive explanations(SHAP)model was proposed. An effectiveness evaluation model was established using the XGBoost algorithm based on an evaluation index system. The mapping relationship between index data and combat effectiveness was analyzed and verified using simulation data to mine the combat effectiveness of the UAV cluster system. The PSO algorithm was employed to optimize the hyperparameters of the XGBoost model to enhance evaluation accuracy and efficiency. To balance the predictability and interpretability of the evaluation work, the SHAP mechanism was utilized to interpret the effectiveness evaluation process and identify directions for index optimization. Verified by UAV cluster combat simulation data collected via the Vensim platform, the proposed model demonstrated superior accuracy and interpretability compared to support vector regression(SVR), random forest(RF)algorithm, light gradient boosting machine(LightGBM), and back propagation(BP)neural network methods.

Key words: UAV cluster, effectiveness evaluation, machine learning, XGBoost, SHAP

中图分类号:

TP181

张水库,张伦,龚建兴,黄健. 基于优化XGBoost的无人机集群作战效能评估及分析[J]. 山东大学学报 (工学版), 2026, 56(2): 11-18.

ZHANG Shuiku, ZHANG Lun, GONG Jianxing, HUANG Jian. Evaluation and analysis of unmanned aerial vehicle cluster combat effectiveness based on optimized XGBoost[J]. Journal of Shandong University(Engineering Science), 2026, 56(2): 11-18.

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