摘要
无线传感器网络(Wireless Sensor Network,WSN)系统性能的提高,离不开对WSN中每一个传感器节点地理位置的精准定位。全局人工蜂群算法在基本人工蜂群算法的基础上,在邻域搜索后将迭代最优解添加到新解的更新公式中,提高了算法的开发能力。但将其应用于WSN节点位置求解时,存在计算时间长、收敛不稳定的问题。提出一种改进的全局人工蜂群算法,在邻域搜索后对新解进行衡量,若新解适应值在可接受的范围内,与迭代最优解进行交叉操作;若新解适应值较好,不与迭代最优解进行交叉操作;若新解适应值较差,舍弃新解。这较好地平衡了算法的探索和开发能力。求解WSN节点位置时,证明了该算法有更快的收敛速度和更好的收敛效果。
The overall performance of wireless sensor network（WSN） is highly reliable of the accurate geographic loca- tion of each sensor node in WSN. Based on the artificial Bee colony algorithm, the gbest-guided artificial bee colony algo- rithm adds the iterative optimal solution to updating formula after the neighborhood search, improving the development ability of the algorithm. But when it is applied to WSN node location, it still has the problem of long computing time and unstable convergence. An improved Gbest-guided artificial bee colony algorithm was proposed, and we measured the new solution after neighborhood search. If the new solution is acceptable, crossover with the iterative optimal solution is exe- cuted. If the new solution is good, crossover operation is not executed. If the new solution is bad, the solution is quitted. It balances the exploration and development ability of the algorithm better, and it＇s proved to have faster convergence rate and better convergence effect when applied to WSN node location.
出处
《计算机科学》
CSCD
北大核心
2016年第12期273-276,共4页
Computer Science
关键词
WSN节点定位
RSSI
人工蜂群算法
全局人工蜂群算法
WSN nodes localization, RSSI, Artificial bee colony algorithm, Gbest-guided artificial bee colony algorithm
