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基于简化人工蜂群算法的地面防空火力拦截设备部署方法
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TP182

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四川省科技计划(2023YFG0316);中国高校产学研创新基金(2021ITA10016);西华大学校重点项目(Z1320929);四川省产业发展专项基金(ZYF-2018-056)


A Deployment Method of Ground-Based Air Defense Intercept Systems by Using Simplified Artificial Bee Colony Algorithm
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    摘要:

    针对地面防空火力拦截设备部署问题中部署方案产生速度慢、不符合战场实际环境问题,提出了一种基于简化人工蜂群算法的地面防空火力拦截设备部署方法。本方法在地面防空火力拦截设备部署方案制定过程中,将人工蜂群算法分为初始化阶段和优化阶段。从专家知识辅助的视角出发,初始化阶段利用专家知识对可部署位置进行了寻优处理并结合随机初始化,优化阶段利用简化型邻域优化对初始化阶段产生的方案进行优化。2个阶段均对产生的部署方案进行校验,若方案达到给定突防概率指标则直接保存输出,大大提高了收敛速度,且产生的部署方案符合实际。仿真结果表明:提出的方法相比于传统蜂群算法在收敛速度方面具有明显优势。

    Abstract:

    Aimed at the problems that the deployment of ground-based anti-aircraft fire interception equipment is slow at speed and the deployment scheme does not conform to the actual environment of the battlefield,a deployment method of ground-based air defense intercept systems by using simplified artificial bee colony algorithm is proposed. In this method, in the process of formulating the deployment scheme of ground-based anti-aircraft fire interception equipment, the artificial bee colony algorithm is divided into an initialization stage and an optimization stage. In perspective of expert knowledge assistance, the initialization stage is intended to utilize the expert knowledge for optimizing the deployable location in combination with random initialization, and the optimization stage is intended to utilize the simplified neighborhood optimization for optimizing the scheme generated in the initialization stage. The resulting deployment schemeis verified in both stages, and the output is directly saved if the scheme reaches the given penetration probability index, greatly improving the convergence speed, and the resulted deployment scheme is realistic.The simulation results show that the proposed method has obvious advantages over the traditional bee colony algorithm at convergence speed, and can conform well to the battlefield environment.

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刘涛, 刘宇畅, 赵桂毅, 卿朝进, 宋建军.基于简化人工蜂群算法的地面防空火力拦截设备部署方法[J].空军工程大学学报,2024,25(1):52-58

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  • 在线发布日期: 2024-02-20
  • 出版日期: 2024-02-25