引用本文:孙海文,谢晓方,庞威,等.基于改进火力分配模型的综合防空火力智能优化分配[J].控制与决策,2020,35(5):1102-1112
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基于改进火力分配模型的综合防空火力智能优化分配
孙海文1, 谢晓方1, 庞威2, 孙涛1, 王诚成1
(1. 海军航空大学兵器科学与技术系,山东烟台264001;2. 31102部队,南京210000)
摘要:
针对综合防空火力分配中,传统火力分配模型没有全面考虑火力通道射击效能因素,且在火力资源相对充足的情况下火力资源易浪费和易延误战机的问题,将射击有利度、飞临时间与威胁度等因素结合,构建一种新的综合防空火力分配模型.基于此模型,针对来袭目标、火力节点以及制导节点3者之间的火力优化匹配问题,提出一种基于深度优先搜索预处理的多种群并行布谷鸟搜索算法(PMPCSA).该方法采用Prolog智能规划语言构建目标-火力节点-制导节点匹配规则库,在规则库中利用深度优先搜索快速生成可行的目标-火力节点-制导节点的匹配预处理方案;采用多种群并行布谷鸟搜索算法,对得到的可行分配空间进行搜索,通过引入多种群并行优化搜索,不同种群赋予不同控制参数的思想,兼顾算法的全局探索和局部开发能力,可有效提高算法寻优效率.最后,通过仿真结果验证所建综合防空火力分配模型的优势性,同时表明,所提出的PMPCSA算法能有效地平衡全局探索与局部开发,在保证较高收敛速度的同时,提高全局探索能力.
关键词:  综合防空火力优化分配  射击有利度  飞临时间  Prolog智能规划  深度优先搜索  多种群并行布谷鸟搜索算法
DOI:10.13195/j.kzyjc.2018.1019
分类号:N945.25;E920.8
基金项目:中国博士后科学基金项目(2013T60923).
Integrated air defense firepower intelligence optimal assignment based on improved firepower assignment model
SUN Hai-wen1,XIE Xiao-fang1,PANG Wei2,SUN Tao1,WANG Cheng-cheng1
(1. Department of Weapons Science and Technology,Naval Aeronautical University, Yantai 264001,China;2. Unit 31102 of PLA,Nanjing 210000,China)
Abstract:
In air defense firepower assignment, the traditional fire distribution model does not fully consider the firing efficiency factors of the fire channel, and under the condition of relatively sufficient fire resources, firepower resources are easy to waste and damage time could be delayed. By combining the fire advantage, flying time and threat degree, an new integrated air defense firepower assignment model is constructed. On this basis, a preprocessing multi group parallel cuckoo search algorithm (PMPCSA) is proposed for the optimal matching problem between the coming target, fire node and guidance nodes. The method uses Prolog intelligent planning language to build a target-fire node-guidance node matching rule library. In the rule library, the depth-first search is used to quickly generate a feasible matching pre-processing scheme of target-fire node-guidance nodes. Then, the multi group parallel cuckoo search algorithm is used to search the feasible allocation space. By introducing the idea of multi-population parallel optimization search and different populations given different control parameters, both the global exploration and the local development ability of the algorithm are taken into account, and the efficiency of algorithm optimization is effectively improved. Finally, the simulation results show the superiority of the integrated air defense firepower assignment model. At the same time, the results show that the proposed PMPCSA can effectively balance the global exploration and local development, which improves the global exploration ability while ensuring higher convergence speed.
Key words:  integrated air defense firepower intelligence optimal assignment  fire advantage  flying time  Prolog intelligent planning  depth first search  multi group parallel cuckoo search algorithm

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