基于粒子群优化和大规模邻域搜索算法的导弹火力资源调度方法  

Missile Firepower Resource Scheduling Method Based on Particle Swarm Optimization and Large-Scale Neighborhood Search Algorithm

作　　者：陆志沣 汪达旺 邱子彰 伍国华 LU Zhifeng;WANG Dawang;QIU Zizhang;WU Guohhua(School of Traffic&Transportation Engineering,Central South University,Changsha 410078,China;School of Automation,Central South University,Changsha 410078,China)

机构地区：[1]中南大学交通运输工程学院,湖南长沙410078 [2]中南大学自动化学院,湖南长沙410078

出　　处：《系统仿真技术》2024年第4期371-381,共11页System Simulation Technology

摘　　要：在现代海上防空反导作战中,海上护航编队需要对已跟踪的目标进行导弹火力资源分配。面向海上防空反导场景,针对小规模场景和大规模场景,分别提出基于粒子群算法的调度方法和基于大规模邻域搜索算法的调度方法。在不同规模的海上舰队作战场景下,根据目标态势和舰队导弹资源构建导弹火力资源数学模型,对提出的2种调度方法在不同规模的防空反导场景下进行仿真实验,实验结果表明,2种调度算法在对应的场景下可有效提升拦截率和效费比,从而验证了所提方法的有效性。In modern naval air defense and missile defense operations,naval escort fleets need to allocate missile firepower resources to the tracked targets.This paper focuses on the naval air defense and anti-missile scenario,and proposes two scheduling methods,one is a particle swarm optimization(PSO)algorithm-based scheduling method for small-scale scenarios,and the other is a large neighborhood search-based scheduling method for large-scale scenarios.In different naval fleet combat situations,missile firepower resource mathematical models are constructed based on the target situation and fleet missile resources,based on which simulations were conducted to evaluate the performance of the two scheduling methods in various defense and missile defense scenarios.The experimental results show that both scheduling algorithms effectively improve the interception rate and cost-effectiveness ratio in their respective scenarios,validating the effectiveness of the proposed methods.

关 键 词：海上防空反导 智能优化算法 粒子群优化 大规模邻域搜索 武器目标分配 

分 类 号：TP278[自动化与计算机技术—检测技术与自动化装置]