摘要
武器-目标分配是一个至今未能解决好的多约束规划问题,其复杂性包括模型和算法两方面,已被证明是一个NP完全问题。在对以往武器-目标分配模型分析的基础上,引入时间和制导资源约束构建新的模型,以防御武器系统生存概率最大作为目标函数,提出一种混合粒子群算法。该算法融合粒子群算法和遗传算法,首先利用粒子群算法找到不受时间和制导资源约束的一组解,再利用一个遗传算法对粒子群算法找到的解进行寻优,最终找到一组满足时间和制导资源约束的最优解。仿真结果表明,该算法收敛速度快,求解精度高。
Due to the complexities including its model and algorithm, weapon target allocation is a problem of multi-constraint program not settled yet, which has been proved to be a NP complete problem. After analyzing the previous model of weapon target allocation, the constrain of time and resource to construct a new model is introduced and a hybrid particle swarm algorithm to study this problem is put forward based on the obj particle swarm algorith ective function for aerial defense survival maximization. This algorithm combines m and genetic algorithm. Firstly, find a set of solutions which are not constrained by time and resource by using particle swarm algorithm; then find a best solution whic strains of time and resource among the set of solutions by using genetic algorithm. The h meets the consimulation result indicates that this algorithm＇ convergence speed is quick and the precision is high.
出处
《现代防御技术》
北大核心
2010年第4期1-5,10,共6页
Modern Defence Technology
关键词
武器分配与规划
粒子群算法
遗传算法
混合粒子群算法
weapon allocation and scheduling （WAS）
particle swarm algorithm
genetic algorithm
hybrid particle swarm algorithm
