结合量子协同旅鼠算法与<italic style="font-style: italic">K</italic>-modes的航空数据聚类算法

贾深; 黄鹤; 高涛; 王会峰

doi:10.7652/xjtuxb202610009

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结合量子协同旅鼠算法与K-modes的航空数据聚类算法

专题智能感知与自主决策 | 更新时间：2026-09-29

- 结合量子协同旅鼠算法与K-modes的航空数据聚类算法
- An Aviation Data Clustering Algorithm Combining the Quantum Collaborative Artificial Lemming Algorithm withK-modes
- 西安交通大学学报 2026年60卷第10期页码：103-112
- 作者机构：
  
  长安大学电子与控制工程学院，710064，西安
  长安大学智慧高速公路信息融合与控制重点实验室，710064，西安
- 作者简介：
  
  贾深（2001-），男，硕士生；
  黄鹤（通信作者），男，教授，博士生导师。
- 基金信息：
  
  国家自然科学基金资助项目(52572353)
- DOI：10.7652/xjtuxb202610009
  中图分类号： TP301.6
- 收稿：2026-01-16，
  
  网络首发：2026-04-15，
  
  纸质出版：2026-10-10
- 稿件说明：
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贾深, 黄鹤, 高涛, 等. 结合量子协同旅鼠算法与K-modes的航空数据聚类算法[J/OL]. 西安交通大学学报,2026,60 (10):103-112. https://doi.org/10.7652/xjtuxb202610009.

JIA Shen, HUANG He, GAO Tao, et al. An Aviation Data Clustering Algorithm Combining the Quantum Collaborative Artificial Lemming Algorithm withK-modes[J/OL]. Journal of Xi'an Jiaotong University,2026,60 (10):103-112. https://doi.org/10.7652/xjtuxb202610009.
贾深, 黄鹤, 高涛, 等. 结合量子协同旅鼠算法与K-modes的航空数据聚类算法[J/OL]. 西安交通大学学报,2026,60 (10):103-112. https://doi.org/10.7652/xjtuxb202610009. DOI：

JIA Shen, HUANG He, GAO Tao, et al. An Aviation Data Clustering Algorithm Combining the Quantum Collaborative Artificial Lemming Algorithm withK-modes[J/OL]. Journal of Xi'an Jiaotong University,2026,60 (10):103-112. https://doi.org/10.7652/xjtuxb202610009. DOI：

摘要

针对低空经济背景下航空运行数据离散属性多、异构性强，以及

-modes聚类存在初始中心敏感、易陷入局部最优和复杂数据适应能力不足等问题，提出一种量子协同人工旅鼠算法（QCALA）优化的

-modes聚类算法（QCALA-

-modes）。构建QCALA-

-modes算法，将量子隧穿初始化引入聚类中心生成过程，并设计自适应步长扰动、多洞穴协同挖掘、量子混沌螺旋觅食和三模式切换逃逸这4种策略，在能量因子调控下实现全局搜索与局部开发协同优化。采用8个标准测试函数、6个公开数据集及和Heywhale社区539383条航班数据进行验证。在公开数据集上：QCALA-

-modes在Aggregation数据集上的归一化互信息（normalized mutual information，NMI）为0.9062，优于

-means的0.8395和层次聚类的0.9010；在Bupa数据集上的NMI为0.1384，高于

-means的0.0009和层次聚类的0.0001；在Records数据集上的NMI为0.5319。4类航班模式延误率簇间极差达到35.44%，有效区分了高低风险运营模式，验证了所提算法的聚类辨识力与业务可解释性。所提算法可为低空经济场景中航空运行状态精细化识别与延误风险分级管控提供方法支撑。

Abstract

To address the challenges posed by aviation operational data in the context of the low-altitude economy—characterized by numerous discrete attributes and high heterogeneity—as well as the inherent limitations of the

-modes clustering algorithm

including sensitivity to initial cluster centers

susceptibility to local optima

and insufficient adaptability to complex data

this paper proposes a

-modes clustering algorithm optimized by the quantum collaborative artificial lemming algorithm (QCALA). The QCALA-

-modes algorithm is constructed by introducing quantum tunneling initialization into the cluster center generation process and designing four strategies: adaptive step-size perturbation

multi-burrow collaborative excavation

quantum chaotic spiral foraging

and tri-mode switching escape. These strategies achieve synergistic optimization of global exploration and local exploitation un

der the regulation of an energy factor. The proposed method is validated using 8standard benchmark functions

6public datasets

and 539383flight records from the Heywhale community. On the public datasets

QCALA-

-modes achieves a normalized mutual information (NMI) of 0.9062on the Aggregation dataset

outperforming

-means (0.8395) and hierarchical clustering (0.9010); on the Bupa dataset

its NMI reaches 0.1384

surpassing

-means (0.0009) and hierarchical clustering (0.0001); and on the Records dataset

the NMI is 0.5319. For the flight data

the inter-cluster range of delay rates among the four flight patterns reaches 35.44%

effectively distinguishing between high-and low-risk operational modes and validating the clustering discriminability and business interpretability of the proposed algorithm. The proposed algorithm provides methodological support for the refined identification of aviation operational statuses and the tiered management of delay risks in low-altitude economy scenarios.

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Keywords

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结合量子协同旅鼠算法与K-modes的航空数据聚类算法

An Aviation Data Clustering Algorithm Combining the Quantum Collaborative Artificial Lemming Algorithm withK-modes

DOI：10.7652/xjtuxb202610009

摘要

Abstract

关键词

Keywords

references