吉林大学学报(信息科学版)

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改进蜂群算法的 WSN 节点分布优化研究

付光杰, 胡明哲, 乔永娜   

  1. 东北石油大学 电气信息工程学院, 黑龙江 大庆 163318
  • 收稿日期:2017-03-28 出版日期:2017-09-29 发布日期:2017-10-23
  • 作者简介:付光杰(1962— ), 女, 吉林通化人, 东北石油大学教授, 博士生导师, 主要从事电力电子与电力传动、 无线传感器网络研究, (Tel)86-459-6503828(E-mail)657533415@ qq. com。
  • 基金资助:
    东北石油大学国家基金校内培育基金资助项目(py120219); 东北石油大学研究生创新科研项目资助(YJSCX2016-029NEPU)

WSN Node Distribution Optimization Based on Improved Bee Colony Algorithm

FU Guangjie, HU Mingzhe, QIAO Yongna   

  1. School of Electrical Engineering and Information, Northeast Petroleum Unicersity, Daqing 163318, China
  • Received:2017-03-28 Online:2017-09-29 Published:2017-10-23

摘要: 为合理部署无线传感器网络节点, 减少目标区域的覆盖盲区, 提出了基于择优型全局人工蜂群算法的优
化方案。 改进算法引入择优机制对各蜜源进行区分, 借鉴差分进化变异策略对优等蜜源进行邻域搜索, 采用全
局引导机制对劣等蜜源进行寻优, 提高迭代效率、 收敛速度以及全局搜索能力。 将此算法应用于 WSN
(Wireless Sensor Network)节点分布优化问题, 并与人工蜂群算法、 全局人工蜂群算法的优化结果进行比较。
仿真结果表明, 与这两种算法相比, 平均覆盖率提高 1% 以上, 最差覆盖率提高 2% 以上。 该算法的节点优化
方案对目标区域的覆盖性能明显优于其他两种算法, 有效提高了 WSN 的感知性能。

关键词: 节点分布, 差分进化, 覆盖率, 人工蜂群算法, 收敛速度, 无线传感器网络, 择优机制, 全局引导机制

Abstract:  In order to rationally deploy the wireless sensor network nodes and reduce the coverage area of the
target area, an optimization scheme based on the optimal global artificial bee colony algorithm is proposed. The
improved algorithm introduces the selection mechanism to distinguish the honey sources. The better ones learn
from the differential evolution strategy to search neighborhood, and the inferior ones use the global guidance
mechanism to optimize nectar. It will improve the iterative efficiency, convergence speed and global search
ability. This algorithm is applied to the WSN(Wireless Sensor Network) node distribution optimization problem,
and compared with the optimization results of artificial bee colony algorithm and global artificial bee colony
algorithm. The simulation results show that the average coverage is at least 1% higher and the worst coverage is
at least 2% higher than the two algorithms. It can be seen that the coverage optimization of the target area based
on this algorithm is superior to the other two algorithms, which effectively improves the perceived performance
of WSN.

Key words: node distribution, preferred mechanism, coverage rate, differential evolution,  wireless sensor network (WSN), artificial bee colony algorithm, global guiding mechanism, convergence speed

中图分类号: 

  • TP212. 9