基于量子狼群进化的多目标汇聚节点覆盖算法

发布时间：2018-02-15 06:36

本文关键词： 无线传感器网络量子狼群进化算法覆盖多目标汇聚节点　出处：《电子与信息学报》2017年05期 　论文类型：期刊论文

【摘要】：在构建双层无线传感器网络中,汇聚层覆盖需要考虑无重复覆盖面积、汇聚节点连通性和能耗平衡这3个关键问题。该文将上述3个问题统筹为多目标优化难题(MOP),提出一种面向汇聚节点覆盖的量子狼群进化算法(QWPEA),选择出候选头狼(CLW)群体,以滑模交叉、量子旋转门、非门变异等方法产生寻优高效的下一代量子编码人工狼。仿真结果表明,该文所提算法能够有效减少汇聚节点数,提高汇聚层结构稳定性,并平衡网络能耗,适于大范围,大规模传感器节点网络部署环境。在800 m×800 m面积部署传感器节点达到1000个时,汇聚有效覆盖率较MOPSO,NSGA-II算法分别高29.55%和25.93%,汇聚通信能耗率分别高15.27%和18.63%,汇聚占通率分别低14.01%和15.46%。
[Abstract]:In the construction of two-layer wireless sensor networks, the convergence layer coverage needs to consider the coverage area without repetition. The connectivity and energy balance of convergence nodes are three key problems. In this paper, the above three problems are integrated into the multi-objective optimization problem, and a quantum wolf swarm evolutionary algorithm (QWPEAA) for convergence node coverage is proposed, and the candidate CLW population is selected. Sliding mode crossover, quantum rotary gate and non-gate mutation are used to generate the next generation quantum coding artificial wolf. Simulation results show that the proposed algorithm can effectively reduce the number of convergence nodes and improve the stability of convergence layer structure. And balance network energy consumption, suitable for large-scale, large-scale sensor node network deployment environment. When 800m 脳 800m area deployment sensor nodes reach 1000, The convergent effective coverage is 29.55% and 25.93 higher than that of MOPSON NSGA-II, the energy consumption of convergent communication is 15.27% and 18.63, and the convergent occupancy is 14.01% and 15.466.The convergent effective coverage is higher than that of MOPSON NSGA-II, respectively.
【作者单位】：天津大学电气自动化与信息工程学院;天津市公安消防局;
【基金】：国家自然科学基金(61571318) 青海省科技项目(2015-ZJ-904) 海南省科技项目(ZDYF2016153)~~
【分类号】：TP212.9;TN929.5

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