基于改进遗传算法的不规则面目标瞄准点选择

Selecting Optimization of Irregular Surface Target's Aimpoints Based on Improved Genetic Algorithm

采用传统遗传算法能够有效地对不规则面目标的瞄准点进行优化选择,但存在优化速度慢、计算精度不高的缺点。在传统遗传算法的基础上,改进了适应值的标定方法,引进了基于相似度的群体多样性优化方法,改善了群体的多样性,有效避免了早熟现象的出现。成功将改进遗传算法应用于不规则面目标的瞄准点优化选择问题,仿真结果表明,对不规则面目标的瞄准点进行优化选择时,改进遗传算法与传统遗传算法相比,提高了运算速度和全局寻优能力。

The speed and precision of the traditional Genetic Algorithm in solving the selection optimization of irregular surface target＇s aimpoints is slow and low.Based on the traditional Genetic Algorithm,the fitness of the calibration method is improved,a similarity-based optimization method of population diversity is introduced to improve the population＇s diversity and avoid the premature phenomenon.The improved Genetic Algorithm is successfully applied to the problem of irregular surface target＇s optimization aimpoints.The simulation result indicates that the improved Genetic Algorithm raises the computation speed and global search capability.