联合弹药毁伤复杂面目标瞄准点高效优化算法  

An Efficient Aiming Points Optimization Algorithm for Joint Missiles Dstroying Area Targets with Complex Shapes

作　　者：晏江 尹鹏 刘彦[1,2] 张文宇 黄风雷[1] YAN Jiang;YIN Peng;LIU Yan;ZHANG Wenyu;HUANG Fenglei(State Key Laboratory of Explosion Science and Safety Protection,Beijing Institute of Technology,Beijing 100081,China;Yangtze Delta Region Academy,Beijing Institute of Technology,Jiaxing 314000,Zhejiang,China;School of Intelligence Science and Technology,University of Science and Technology Beijing,Beijing 100083,China)

机构地区：[1]北京理工大学爆炸科学与安全防护全国重点试验室,北京100081 [2]北京理工大学长三角研究院,浙江嘉兴314000 [3]北京科技大学智能科学与技术学院,北京100083

出　　处：《兵工学报》2025年第4期150-162,共13页Acta Armamentarii

摘　　要：针对形状不规则复杂面目标多弹瞄准点优化算法计算效率低、稳定性差、优化能力不足的问题,提出一种基于弹药圆概率偏差(Circular Error Probable,CEP)的毁伤概率矩阵库(Damage Probability Matrix Library,DPML)和改进启发式退火优化机制的高效瞄准点优化算法(Efficient Aiming Point Optimization Algorithm,EAPOA)。构建多弹瞄准点优化模型时,除考虑目标形状、导弹毁伤能力外,还考虑导弹直接毁伤、间接毁伤和多弹种联合毁伤等复杂因素对目标毁伤效果的影响。提出一种基于DPML的毁伤概率快速估计算法,提升算法优化效率和鲁棒性;设计一种基于候选瞄准点序列化的优化算法框架,并提出基于全局搜索和改进退火机制的启发式优化算法,降低瞄准点组合求解空间大小并提升算法优化能力。通过6个复杂面目标测试用例验证算法性能。研究结果表明,所提的EAPOA相比于增强精英保留策略遗传算法具有更强的优化能力,且平均优化时间仅为其1/5~1/3,在优化收益和计算效率上具有明显优势。The multi-missile aiming points optimization algorithm has the low computation efficiency,weak stability and insufficient optimization capability for the area targets with complex shapes.An efficient aiming point optimization algorithm(EAPOA)based on the damage probability matrix library(DPML)of circular error probable(CEP)and the improved simulated annealing mechanism is proposed.An optimization model of multi-missile aiming points is developed,which takes into account the impacts of the direct damage,the indirect damage,and the combined damage of multi-missiles on the Target damage effect except for target shape irregularity and missile damage capacity.The proposed DPML method can improve the optimization efficiency and robustness of damage probability estimation algorithm.In addition,a candidate aiming point set-based optimization framework is designed,and a heuristic optimization method based on a global searching and improved simulated annealing that is helpful for escaping from local minima is developed.The performance of the proposed algorithm is verified by using six complex area target test cases.The results show that the proposed EAPOA has stronger optimization ability compared with the genetic algorithm with enhanced elite retention strategy,and the average optimization time is only 1/5-1/3,which has obvious advantages in optimization income and computational efficiency.

关 键 词：联合弹药 毁伤评估 瞄准点优化 启发式算法 复杂面目标 毁伤概率 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]