摘要
无人机舰机协同任务规划技术是指充分利用无人机与舰艇的优势互补,协同进行作战任务规划的新技术,它是无人机任务规划问题的研究新热点,对于提升海军海上作战能力具有重要意义。针对该问题提出了相应的数学模型,并利用自适应的粒子群算法(self-adaptive particle swarm optimization,APSO)进行了求解,该算法能够自适应调整粒子群的惯性权重,更好的防止粒子群陷入局部最优。实验表明,在给定的实验样本中APSO相对于标准粒子群算法和带有压缩因子的粒子群算法能更有效的求解。
Cooperative task planning for ship and unmanned aerial vehicles （UAVs） （CPSU）is a new tech- nology which can make full use of the complementary advantages between ship and UAVs to make task planning cooperatively. It is a new focus on the UAVs＇ task planning problem, and it has great influence on improving the navy combat capability. A mathematical model of CPSU is built, and then a selbadaptive particle swarm op- timization （APSO） algorithm is introduced to solve it. The algorithm can self-adaptively change the inertia weight, which can avoid the PSO trapping into the local optimum better. The experiment shows that the APSO algorithm solves the problem more effectively than the standard PSO and the PSO with the constrict factor.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2016年第7期1583-1588,共6页
Systems Engineering and Electronics
基金
国家自然科学基金(71472058
71401048)
教育部人文社科项目(13YJC630051)
安徽省自然科学基金项目(1508085MG140)资助课题
关键词
任务规划
舰机协同
粒子群算法
自适应
task planning
cooperative use of ships and unmanned aerial vehicles （UAVs）
particle swarm optimization （PSO）
self-adaptive
