基于量子狼群进化的多目标汇聚节点覆盖算法

金杉; 金志刚

doi:10.11999/JEIT160693

基于量子狼群进化的多目标汇聚节点覆盖算法

doi: 10.11999/JEIT160693

金杉¹,
金志刚²

2.
(天津大学电气自动化与信息工程学院天津 300072) ②(天津市公安消防局天津 300020)

基金项目:

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)

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- 被引次数: 0
出版历程
- 收稿日期: 2016-07-04
- 修回日期: 2016-12-09
- 刊出日期: 2017-05-19

Multi-objective Sink Nodes Coverage Algorithm Based on Quantum Wolf Pack Evolution

JIN Shan¹,
JIN Zhigang²

2.
(Electrical, Automation and Information Engineering College, Tianjin University, Tianjin 300072, China)

Funds:

The National Natural Science Foundation of China (61571318), The Qinghai Province Science and Technology Program (2015-ZJ-904), The Hainan Province Science and Technology Program (ZDYF2016153)

摘要

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 m800 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%。
- 无线传感器网络 /
- 量子狼群进化算法 /
- 覆盖 /
- 多目标 /
- 汇聚节点
Abstract: Satisfying non-repeated coverage, connectedness, and energy balance of sink layer are critical problems in multi-layers Wireless Sensor Networks (WSNs). They are overall planed as a Multi-objective Optimization Problem (MOP). For resolving it, the Quantum Wolf Pack Evolutionary Algorithm (QWPEA) is proposed, which actualizes the Candidate Leader Wolf (CLW) selection, sliding mode crossing, quantum rotating gate, and NOR gate mutation are used to obtain the more accurate wolfs location. Simulation results show that QWPEA can minus the number of sink nodes, promote the steadiness, and balance the energy consumption in a huge scale of WSNs effectively. While 1000 sensors are deployed on 800 m800 m with QWPEA, the sink effective coverage ratio is higher than either MOPSO as 29.55% or NSGA-II as 25.93%. And the sink communication energy consumption ratio is higher than the latter two methods as 15.27% and 18.63% separately. Also, the sink occupied ratio is lower than them as 14.01% and 15.46% severally.
- Wireless sensor networks (WSNs) /
- Quantum Wolf Pack Evolutionary Algorithm (QWPEA) /
- Coverage /
- Multi-objective /
- Sink node

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