基于PSO-GA混合算法的末端防御兵力优化部署方法  

End-Defense Force Optimization Deployment Method Based on PSO-GA Hybrid Algorithm

作　　者：温包谦 王涛 成坤 张济众 WEN Baoqian;WANG Tao;CHENG Kun;ZHANG Jizhong(Rocket Force University of Engineering,Xi’an 710025,China;Rocket Force Equipment Department,Beijing 100085,China)

机构地区：[1]火箭军工程大学,西安710025 [2]火箭军装备部,北京100085

出　　处：《兵器装备工程学报》2019年第11期45-49,共5页Journal of Ordnance Equipment Engineering

基　　金：装备军内科研项目(JJ20172A04134)

摘　　要：针对具有大规模、多约束、非线性特点的要地末端防御兵力优化部署问题,建立了基于火力覆盖能力的末端防御兵力优化部署模型,并提出了一种基于粒子群与遗传算法的模型求解方法,该方法克服了进化算法的随机性,加快了搜索速度,有效防止算法的早熟收敛。仿真实验证实了构造的混合算法能够有效对模型求解,为科学制定要地末端防御优化部署方案提供作战建议。Aiming at the problem of optimal deployment of end-defense forces with large-scale,multi-constrained and non-linear characteristics,an optimal deployment model of end-defense force based on fire coverage capability was established,and a particle swarm optimization algorithm based on particle swarm optimization and genetic algorithm(PSO-GA)was proposed.The model solving method overcomes the randomness of the evolutionary algorithm,speeds up the search speed,and effectively prevents the premature convergence of the algorithm.The simulation experiment proves that the constructed hybrid algorithm can effectively solve the model,and can provide operational advice for the scientific development of the end-defense optimization deployment plan.

关 键 词：要地防空 优化部署 火力单元 末端防御 粒子群算法 遗传算法 

分 类 号：E955[军事—军事工程]