PSO算法在MAV群并行仿真试验中的应用研究  

Application of PSO Algorithm in MAV Swarm Parallel Simulation

作　　者：王再社[1] 李革[1] 张耀程[1] 欧微[1] 

机构地区：[1]国防科技大学机电工程与自动化学院,湖南长沙410073

出　　处：《计算机仿真》2009年第5期61-63,67,共4页Computer Simulation

摘　　要：利用MAV群执行搜索任务具有安全、快速、高效等优点,无论在军用还是民用方面都将发挥不可替代的作用。考虑到MAV群的续航能力和提高搜索效率的需要,在执行搜索任务的时候首先确定一条"最短"路径至关重要。寻找最短路径问题已经有许多成熟的方法,研究的是采用粒子群优化算法求解最短路径的问题。与其他求解TSP问题的方法相比,粒子群优化算法具有概念简单、鲁棒性好、智能背景深刻等优点;尤其重要的是它天生具有并行计算的潜质,适于并行化后应用到并行仿真中去。实现了PSO算法的并行化,并验证了运行结果的正确性。It is safe, quick, and efficient to perform a reconnaissance mission with MAV swarms. They have an important role to play in both military and civil usage and can not be substituted. Because MAV swarm＇ s flying capability is limited, also considering the need to improve their searching efficiency, it is important to find a ＂shortest＂ route before starting a reconnaissance mission. Numerous methods have been proposed to find a ＂shortest＂ route, but this paper tries to solve this problem with Particle Swarm Optimization Algorithm. Particle Swarm Optimization Algorithm is simpler, more robust, and more intelligent than other methods. Most importantly, it is naturally suitable for parallel computation, and can be easily parallelized and used in Parallel Simulation. This paper provides a parallel flow chart of PSO algorithm and proves its correctness with experiments.

关 键 词：微型飞行器群 粒子群优化算法 旅行商问题 并行仿真 

分 类 号：V271.4[航空宇航科学与技术—飞行器设计]