基于PSO-AFSA算法的典型集群目标瞄准点选择  

Representative Congregative Targets Aim-points Selection Based on PSO-AFSA Algorithm

作　　者：张高瑜[1] 刘新学[1] 郭飞帅[1] 

机构地区：[1]第二炮兵工程学院,西安710025

出　　处：《火力与指挥控制》2012年第11期116-119,共4页Fire Control & Command Control

摘　　要：利用粒子群算法快速的局部收敛性和人工鱼群的全局收敛性,提出了基于粒子群的人工鱼群混合优化算法,并用于求解常规导弹打击集群目标瞄准点选择优化问题。仿真结果表明:此算法在求解常规导弹打击集群目标瞄准点选择问题时,可以较少的迭代次数取得比较满意的瞄准点。Based on the quickly local convergent performance of particle swarm optimization （PSO） and the global convergent performance of artificial fish swarm algorithm （AFSA） ,a hybrid particle swarm optimization algorithm is proposed. The PSO-ASFA is used to solve aim-points optimization by conventional missile to attack representative congregative targets. The simulation shows,being compared by the conventional PSO, the PSO-AFSA algorithm is more likely to get a good optimization results by less iterative times.

关 键 词：集群目标 人工鱼群算法 粒子群算法 瞄准点优化 

分 类 号：TJ765[兵器科学与技术—武器系统与运用工程]