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弹箭与制导学报 >

2024 , Vol. 44 >Issue 1: 41 - 48

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2024.01.007

改进粒子群优化多拦截器目标分配方法研究

  • 苏山 ,
  • 马泽远 ,
  • 张立 ,
  • 周梦平 ,
  • 刘昊东
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  • 上海机电工程研究所,上海 201109

苏山(1999—),男,硕士研究生,研究方向:飞行器制导与控制。

收稿日期: 2023-07-25

  网络出版日期: 2024-12-28

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Research on Multi-interceptor Target Assignment Method Using Improved Particle Swarm Optimization

  • SU Shan ,
  • MA Zeyuan ,
  • ZHANG Li ,
  • ZHOU Mengping ,
  • LIU Haodong
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  • Shanghai Electro-mechanical Engineering Institute,Shanghai 201109,China

Received date: 2023-07-25

  Online published: 2024-12-28

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摘要

为提高多拦截器拦截对抗场景下的目标分配效能,对多拦截器目标分配算法展开研究。首先提出了目标分配问题的离散优化模型,其次设计了一种改进粒子群优化算法,引入变邻域搜索算法解决传统粒子群优化算法容易陷入局部收敛的问题,设计目标分配矩阵以及适应度函数模型解决带约束离散优化问题,同时避免了编解码算法造成的精度损失;再次设计局部跳出算法的启动准则,提高了算法的效率;最后仿真分析表明:改进粒子群优化算法相对传统算法分配效能可提高9.4%,且收敛结果与全局最优偏差不超过0.1%。

关键词： 目标分配; 编解码策略; 粒子群优化; 变邻域搜索

本文引用格式

苏山 , 马泽远 , 张立 , 周梦平 , 刘昊东 . 改进粒子群优化多拦截器目标分配方法研究[J]. 弹箭与制导学报, 2024 , 44(1) : 41 -48 . DOI: 10.15892/j.cnki.djzdxb.2024.01.007

Abstract

To improve the efficiency of target allocation in multi-interceptor interception countermeasure scenario, the multi-interceptor target allocation algorithm is studied. Firstly, a discrete optimization model for target assignment problem is proposed. Secondly, an improved particle swarm optimization algorithm is proposed. Variable neighborhood search algorithm is introduced to solve the problem that the traditional particle swarm optimization algorithm is prone to local convergence, and the target assignment matrix and fitness function model are designed to solve the constrained discrete optimization problem, while avoiding the precision loss caused by codec algorithm. Finally, the starting criterion of the local jump-out algorithm is designed to improve the efficiency of the algorithm. The simulation results show that the improved PSO algorithm can improve the distribution efficiency by 9.4% compared with the traditional algorithm, and the convergence results are less than 0.1% deviation from the global optimal.

Key words： target allocation; encoding and decoding strategy; particle swarm optimization; variable neighborhood search

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