改进的蚁群优化算法在MDITN仿真中的应用

摘要

仿真MD系统是对其研究的重要方法,研究MD系统的一个重要方向是MDITN的仿真研究。在MDITN中,选择一条供信息传输的时延最小路径即最短路径问题是仿真MDITN的关键问题。ACO算法是一种通用的启发式方法,本文介绍了基本ACO算法及其在MDITN中解决最短路径问题的应用,然后提出了一种改进的ACO算法。在改进的ACO算法中,分别提出了对无出边节点和不连通图的处理、启发信息的优化、开发过程函数和信息素更新策略的改进。实验表明,改进的ACO在收敛性方面有所提高,可以更加有效的解决最短路径问题。%Simulation of MD（Missile Defense） system is an important method of investigating it.Investigation of MDITN（MD Information Transmission Network） by simulating it is an important aspect of Investigating MD system.It is a pivotal problem of simulating MDITN that choosing a minimal delay path information transmissing,or saying shortest path problem.Ant Colony Optimization（ACO） is a metaheuristic.In this paper,basic ACO and its application of MDITN＇s shortest path problem is introduced at frist.Then,a new improved ACO is proposed.In the new ACO,the vertexs which outdegree equal zero and unconnected graph is dealed,heuristic information is optimized,improvement in exploiting function and updating gobal pheromone are made.Experimental results show that the improved ACO enhance the convergence speed,is a more effective solvent for shorest path problem.