基于斯坦纳树的雷场网络大面积损坏修复策略  

A Steiner Tree-based Strategy for Repairing Large-scale Damaged Minefield Network

作　　者：董文[1] 方向[1] 范磊[1] 杨力[1] 雷志刚[2] 

机构地区：[1]解放军理工大学野战工程学院,江苏南京210007 [2]中华人民共和国公安部警卫局防爆安检中心,北京100031

出　　处：《兵工学报》2013年第2期197-202,共6页Acta Armamentarii

基　　金：总装备部预先研究项目(ZLY2008424)

摘　　要：战场环境下预先设定的智能雷场网络易受到敌方攻击而导致大面积损坏,雷场网络被分割成数个互不相连的部分从而丧失了通讯功能。通过将问题映射到斯坦纳最小树问题,提出了一种新颖的雷场网络修复策略。首先采用雷场区域网格模型限制算法的搜索空间,随后引入蛙跳和离散量子粒子群混合优化(JF-QDPSO)算法确定中续节点位置,修复受损网络。仿真实验表明该策略能够有效的恢复网络拓扑结构,算法计算较快,与其它算法相比,重建后的网络通信能耗小,网络生存周期长。Due to the harsh battlefield surroundings and violent nature of intelligent minefield network ap- plications, the minefield network sometimes suffers a large-scale damage that involves several nodes and would thus create multiple disjoint partitions. A novel strategy for repairing such damage was investigated by modeling this problem as Steiner minimum tree problem. At first, a grid model of minefield area that limits the search space to a more manageable size was proposed. And then, according to combinated opti- mi：,.ation algorithm of quantum discrete particle swarm optimization（QDPSO） and jumping frogs optimiza- tion （JFO） , the position of relay nodes was confirmed and the minefield network was repaired. The simu- laton results demonstrate the effectiveness of the proposed recovery algorithm which has less computation- al time. Compared with the other algorithms, the proposed algorithm obtains more promising results in the aw：rage path length and the network life cycle.

关 键 词：人工智能 雷场网络 斯坦纳树 蛙跳优化 离散量子粒子群优化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]