改进的蚁群算法求解无人艇编队火力分配问题  

An Improved Ant Colony Algorithm for Solving Firepower Allocation Problem of USV Formation

作　　者：黄钦龙 刘忠[1] 童继进[1] HUANG Qinlong;LIU Zhong;TONG Jijin(Naval University of Engineering,Wuhan 430000,China)

机构地区：[1]海军工程大学,武汉430000

出　　处：《电光与控制》2020年第8期58-63,74,共7页Electronics Optics & Control

基　　金：湖北省自然科学基金(2018CFC865);中国博士后科学基金(45686)。

摘　　要：针对海上近岸威胁目标速度快、机动性强的特点,为提高无人艇编队海岸防御火力分配问题求解的质量和速度,建立了更加符合战场实际的无人艇编队多约束动态火力分配模型,并利用改进的蚁群算法进行模型求解。该模型较全面地考虑了威胁目标的动态运动变化和多方面资源约束,对算法的搜索能力提出了更高的要求。改进算法通过调整信息素的更新模式,采用时变的挥发因子,能在满足全局搜索的基础上避免陷入局部最优。通过蒙特卡罗仿真对比传统蚁群算法,结果证明该改进算法能有效提高无人艇编队海岸防御火力分配问题求解的有效性、收敛性和实时性,具有一定应用价值。In view of the fast speed and strong maneuverability of offshore threat targets and in order to improve the quality and speed of solving the problem of USV formation’s firepower allocation in coastal defense,a multi-constraint dynamic firepower allocation model of USV formation in line with the actual battlefield is established,and the improved ant colony algorithm is used to solve the model.The model not only considers the dynamic motion of threat targets and multi-facet resource constraints comprehensively,but also places higher demands on the search ability of the algorithm.The improved algorithm can avoid falling into local optimum on the basis of satisfying the global search by adjusting the update mode of the pheromone and adopting the time-varying volatile factor.The result of Monte Carlo simulation shows that:Compared with the traditional ant colony algorithm,the improved algorithm can greatly improve the effectiveness, convergence and real-time performance of solving the USV formation’s firepower allocation problem in coastal defense,and has certain application value.

关 键 词：火力分配 动态模型 蚁群算法 时变挥发因子 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]