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弹箭与制导学报 >

2026 , Vol. 46 >Issue 1: 61 - 76

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2026.01.007

基于变邻域量子粒子群优化的异构反无装备群目标分配方法

  • 张腾 1, 2, 3 ,
  • 王铮 , 1, 2, 3, * ,
  • 王旭阳 4 ,
  • 吴松森 1, 5 ,
  • 韦亚利 1, 6 ,
  • 王晓田 1 ,
  • 宁昕 4 ,
  • 陈占胜 7
展开
  • 1 西北工业大学无人系统技术研究院,陕西 西安 710072
  • 2 西北工业大学无人飞行器技术全国重点实验室,陕西 西安 710072
  • 3 西北工业大学无人机技术集成攻关大平台,陕西 西安 710072
  • 4 西北工业大学航天学院,陕西 西安 710072
  • 5 洛阳电光设备研究所,河南 洛阳 471000
  • 6 上海机电工程研究所,上海 201109
  • 7 上海卫星工程研究所,上海 201109
*

收稿日期: 2025-12-04

  网络出版日期: 2026-03-09

基金资助

国家自然科学基金(62303378)

上海航天科技创新基金(SAST2022-114)

收起

A Target Assignment Method Based on Variable Neighborhood Quantum Particle Swarm Optimization for Heterogeneous Anti-unmanned Equipment Swarms

  • ZHANG Teng 1, 2, 3 ,
  • WANG Zheng , 1, 2, 3, * ,
  • WANG Xuyang 4 ,
  • WU Songsen 1, 5 ,
  • WEI Yali 1, 6 ,
  • WANG Xiaotian 1 ,
  • NING Xin 4 ,
  • CHEN Zhansheng 7
Expand
  • 1 Unmanned Systems Research Institute,Northwestern Polytechnical University,Xi'an 710072,Shaanxi,China
  • 2 National Key Laboratory of Unmanned Aerial Vehicle Technology,Northwestern Polytechnical University,Xi'an 710072,Shaanxi,China
  • 3 Integrated Research and Development Platform of Unmanned Aerial Vehicle Technology,Northwestern Polytechnical University,Xi'an 710072,Shaanxi,China
  • 4 School of Astronautics,Northwestern Polytechnical University,Xi'an 710072,Shaanxi,China
  • 5 Luoyang Institute of Electro-Optical Equipment,Luoyang 471000,Henan,China
  • 6 Shanghai Electro-Mechanical Engineering Institute,Shanghai 201109,China
  • 7 Shanghai Institute of Satellite Engineering,Shanghai 201109,China

Received date: 2025-12-04

  Online published: 2026-03-09

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摘要

为解决现代防空作战中武器-目标分配(Weapon-Target Assignment,WTA)决策效率低与实用性不强的问题,首先构建了一个综合考虑弹药消耗、作战成本、总作战时间与拦截收益四类指标的多目标WTA模型,同时考虑武器-弹药兼容性、弹药库存与毁伤门限等实际约束,以增强模型的实战贴合性。其次,提出了一种混合启发式算法HCQPSO-VNS(Hybrid Chaotic Quantum Particle Swarm Optimization-Variable Neighborhood Search,HCQPSO-VNS)用于求解所提WTA模型。该算法采用Logistic混沌映射提高初始种群质量,利用量子粒子群优化(Quantum particle swarm optimization,QPSO)实现全局搜索,并引入具有多邻域结构的变邻域搜索(Variable Neighborhood Search,VNS)进行局部优化,避免早熟收敛。最后,仿真结果表明,所提算法在较少迭代内即可收敛至高质量可行解,所得分配方案在满足期望毁伤下界与武器-弹药兼容性等约束的同时,可实现四类指标之间的有效均衡。对比仿真显示,该算法综合性能优于多种主流对比算法,可有效提高防空火力分配决策的效率与科学性。同时,随着问题复杂度增加,算法仍能保持较高的寻优效率与计算可接受性,展现出良好的可扩展性。

关键词： 武器目标分配; 多目标优化; 量子粒子群优化; 变邻域搜索

本文引用格式

张腾 , 王铮 , 王旭阳 , 吴松森 , 韦亚利 , 王晓田 , 宁昕 , 陈占胜 . 基于变邻域量子粒子群优化的异构反无装备群目标分配方法[J]. 弹箭与制导学报, 2026 , 46(1) : 61 -76 . DOI: 10.15892/j.cnki.djzdxb.2026.01.007

Abstract

To address the low decision-making efficiency and poor practicality of weapon-target assignment (WTA) in modern air-defense operations,a multi-objective WTA model that comprehensively considers four performance metrics including ammunition consumption,operational cost,total engagement time,and interception benefit is constructed.Practical constraints such as weapon-ammunition compatibility,ammunition inventory,and damage thresholds,etc,are also taken into account to enhance the battlefield applicability of the model.Secondly,a hybrid heuristic algorithm—hybrid ahaotic quantum particle swarm optimization-variable neighborhood search (HCQPSO-VNS) is proposed to solve the proposed WTA model.In the proposed algorithm,a logistic chaotic mapping is employed to improve the quality of the initial population,the quantum particle swarm optimization (QPSO) is utilized for global search; and the variable neighborhood search (VNS) with multiple neighborhood structures is integrated for local optimization to avoid premature convergence.Simulated results demonstrate that the proposed algorithm converges to a high-quality feasible solution within very few iterations.The obtained assignment schemes satisfy the expected lower bounds for damage,weapon-ammunition compatibility,and other constraints,while achieving an effective balance among four performance metrics.Comparative analysis shows that the overall performance of the proposed algorithm outperforms several mainstream algorithms,and can effectively improve the efficiency and scientific rigor of air-defense firepower allocation decisions.Meanwhile,as the problem complexity increases,the proposed algorithm retains high optimization efficiency and acceptable computational load,demonstrating favorable scalability.

Key words： weapon-target assignment; multi-objective optimization; quantum particle swarm optimization; variable neighborhood search

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