摘要
针对多目标多传感器分配中的NP爆炸问题,引入蝙蝠算法进行求解。通过K-均值算法初始化、速度更新采用自适应步长、向反方向搜索及变异操作3项措施对基本蝙蝠算法进行改进,得到改进蝙蝠算法。在仿真实验中,一方面将改进蝙蝠算法和基本蝙蝠算法作对比,证明基本蝙蝠算法在改进后,其计算速度和寻优能力大大提高;另一方面将改进蝙蝠算法与粒子群算法、蜂群算法、狼群算法3种算法作对比,表明改进算法更适用于多传感器多目标分配问题求解,其求解质量更高。
The bat algorithm is introduced to solve the NP problem in multi-sensor muhi-target allocation. By applying the method of K-means for initializing the algorithm, adopting the adaptive step length for updating the speed, searching in the opposite direction and dissociating, the basic bat algorithm is improved. A simulation is made and the result shows that： 1 ） Compared with the basic bat algorithm, the improved bat algorithm has faster calculation speed and better optimizing capability ;and 2） Compared with the particle swarm algorithm, the artificial bee colony algorithm, and the wolf algorithm, the improved bat algorithm is the most adaptive to the problem of multi-sensor multi-target allocation, and can obtain the best solution.
作者
张士磊
张朋
熊志刚
ZHANG Shi-lei;ZHANG Peng;XIONG Zhi-gang(Department of Automatic Control, Henan Institute of Technology, Xinxiang 453003;Chin;2. College of Electronic and Information, Zhongyuan University of Technology, Zhengzhou 451191, Chin;3. Air and Missile Defense College, Air Force Engineering University, Xi＇an 710051, Chin)
出处
《电光与控制》
北大核心
2018年第4期92-96,共5页
Electronics Optics & Control
