基于改进多目标粒子群优化算法的主动声呐浮标布阵优化

毕文豪; 吴宇轩; 许洋; 张安

doi:10.12382/bgxb.2024.0897

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基于改进多目标粒子群优化算法的主动声呐浮标布阵优化

更新时间：2025-10-21

- 基于改进多目标粒子群优化算法的主动声呐浮标布阵优化
- Optimization of Active Sonar Buoy Array Deployment Based on Improved Multi-objective Particle Swarm Optimization Algorithm
- 兵工学报 2025年46卷第9期页码：240897
- 作者机构：
  
  1. 西北工业大学航空学院, 陕西西安 710072
  2. 西北工业大学飞行器基础布局全国重点实验室, 陕西西安 710072
- 作者简介：
  
  * biwenhao@nwpu.edu.cn
- 基金信息：
  
  国家自然科学基金项目(62073267)
- DOI：10.12382/bgxb.2024.0897
  中图分类号：
- 收稿：2024-09-27，
  
  网络出版：2025-09-24，
  
  纸质出版：2025-09-30
- 稿件说明：
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毕文豪, 吴宇轩, 许洋, 等. 基于改进多目标粒子群优化算法的主动声呐浮标布阵优化[J]. 兵工学报, 2025,46(9):240897.

Wenhao BI, Yuxuan WU, Yang XU, et al. Optimization of Active Sonar Buoy Array Deployment Based on Improved Multi-objective Particle Swarm Optimization Algorithm[J]. Acta Armamentarii, 2025, 46(9): 240897.
毕文豪, 吴宇轩, 许洋, 等. 基于改进多目标粒子群优化算法的主动声呐浮标布阵优化[J]. 兵工学报, 2025,46(9):240897. DOI： 10.12382/bgxb.2024.0897.

Wenhao BI, Yuxuan WU, Yang XU, et al. Optimization of Active Sonar Buoy Array Deployment Based on Improved Multi-objective Particle Swarm Optimization Algorithm[J]. Acta Armamentarii, 2025, 46(9): 240897. DOI： 10.12382/bgxb.2024.0897.

摘要

主动声呐浮标作为反潜机广泛使用的探潜设备

研究其布阵优化方法对快速有效地完成反潜作战任务、提升反潜作战的效率有着重要意义。针对现有研究中潜艇位置散布模型与主动声呐浮标搜潜范围简化

难以契合实际场景需求的问题

建立应召搜潜场景下潜艇在概略航向已知和速度未知条件下的散布规律模型与基于主动声呐方程和网格法的主动声呐浮标阵列搜潜效率评定模型;通过引入Kent映射、动态调整的惯性权重和学习因子

提出基于改进多目标粒子群优化算法的浮标布阵优化方法

并在应召搜潜场景下进行仿真验证。仿真结果表明

在不同场景下所提算法均能得到最优部署方案

验证了新算法的可行性和有效性;与其他算法对比

在相同投放量下新算法搜潜概率更大

求解时间更短

证明了新算法的优越性。

Abstract

Active sonar buoy is widely used as a submarine detection equipment for anti-submarine aircraft.It is of great significance to study its deployment optimization method to quickly and effectively complete the anti-submarine tasks and improve the efficiency of anti-submarine combat.The problem in current research lies in the fact that the submarine position distribution model and the simplified search range of active sonar buoys for detecting the submarines are difficult to meet the needs of the actual scenario.A distribution law model of submarines under the conditions of known general heading and unknown speed and an evaluation model of active sonar buoy array search efficiency based on the active sonar equation and the grid method are established.A buoy array deployment optimization method based on improved multi-objective particle swarm optimization algorithm is proposed by introducing Kent mapping

dynamically adjusted inertia weight and learning factor.The simulation verification is carried out in the scene of on-call submarine search.The simulated results show that the proposed algorithm can be used to obtain the optimal deployment scheme in different scenarios

which proves its feasibility and effectiveness.Compared with other algorithms

the proposed algorithm has a larger search probability and a shorter computation time under the condition of the same delivery amount

which proves its superiority.

关键词

Keywords

references

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