基于改进粒子群算法的无线传感器网络覆盖策略
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摘要
针对粒子群算法在无线传感器网络优化方面存在收敛速率慢、容易陷进"早熟"等缺点,本文提出一种基于动态加速因子的粒子群优化算法(PSO-DAC)。该算法主要采用呈线性变化的加速因子以及引入递减的惯性权重系数。实验结果显示,该算法的网络优化覆盖率相比粒子群算法提高了34.6%,比基于递减惯性权重系数的粒子群算法提高了29.3%,证明PSO-DAC算法可有效提高收敛速度以及移动节点覆盖率,从而改善了整个网络的覆盖效果,延伸网络生存周期。
As particle swarm optimization algorithm in the optimization of wireless sensor networks is easy to fall into local optimal solution and slow late convergence as well as other shortcomings,an improved particle swarm optimization algorithm based on dynamic acceleration factor(PSO-DAC)is proposed.It adopts decreasing inertia weight coefficients and introduces dynamic acceleration coefficients.The experimental results show that the algorithm has improved the coverage ratio by 34.6% than that of the basic particle swarm algorithm,which is 29.3% higher than that of the particle swarm algorithm based on decreasing inertia weight coefficient.It is proved that the PSO-DAC algorithm can effectively increase the convergence speed and improve the coverage rate of nodes,so as to improve the coverage effect of the whole network and prolong the network lifetime.
As particle swarm optimization algorithm in the optimization of wireless sensor networks is easy to fall into local optimal solution and slow late convergence as well as other shortcomings,an improved particle swarm optimization algorithm based on dynamic acceleration factor(PSO-DAC)is proposed.It adopts decreasing inertia weight coefficients and introduces dynamic acceleration coefficients.The experimental results show that the algorithm has improved the coverage ratio by 34.6% than that of the basic particle swarm algorithm,which is 29.3% higher than that of the particle swarm algorithm based on decreasing inertia weight coefficient.It is proved that the PSO-DAC algorithm can effectively increase the convergence speed and improve the coverage rate of nodes,so as to improve the coverage effect of the whole network and prolong the network lifetime.
引文
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[2]李劲,岳昆,刘惟一.基于融合的无线传感器网络k-集覆盖的分布式算法[J].电子学报,2013,41(4):659-665.DOI:10.3969/j.issn.0372-2112.2013.04.006.
[3]黄恒杰,龚小龙,王高才.传感器中基于连通支配集的区域覆盖控制算法[J].广西师范大学学报(自然科学版),2016,34(4):19-25.DOI:10.16088/j.issn.1001-6600.2016.04.003.
[4]李菁华,张峥,方达,等.基于混合差分蜂群算法的城市电动汽车充电站布局规划[J].东北电力大学学报,2016,36(4):84-90.DOI:10.3969/j.issn.1005-2992.2016.04.015.
[5]范兴刚,杨静静,王恒.一种无线传感器网络的概率覆盖增强算法[J].软件学报,2016,27(2):418-431.DOI:10.13328/j.cnki.jos.004837.
[6]何璇,郝群,宋勇.一种移动无线视频传感器节点的覆盖算法[J].传感技术学报,2009,22(8):1163-1168.DOI:10.3969/j.issn.1004-1699.2009.08.020.
[7]张清国,李世顺,赵甫哲,等.基于蜂窝结构的混合无线传感器网络覆盖优化算法[J].小型微型计算机系统,2016,37(12):2598-2602.
[8]王蕊,刘国枝.基于鱼群优化算法的无线传感器网络部署[J].振动与冲击,2009,28(2):8-11,24.DOI:10.3969/j.issn.1000-3835.2009.02.003.
[9]黄瑜岳,李克清.基于人工鱼群算法的无线传感器网络覆盖优化[J].计算机应用研究,2013,30(2):554-556.DOI:10.3969/j.issn.1001-3695.2013.02.065.
[10]毛科技,方凯,戴国勇,等.基于改进蚁群算法的无线传感器网络栅栏覆盖优化研究[J].传感技术学报,2015,28(7):1058-1065.DOI:10.3969/j.issn.1004-1699.2015.07.020.
[11]HUANG Ru,ZHU Jie,XU Guanghui.Energy-efficient mechanism based on ACO for the coverage problem in sensor networks[J].Journal of Southeast University(English Edition),2007,23(2):255-260.DOI:10.3969/j.issn.1003-7985.2007.02.021.
[12]吴意乐,何庆,徐同伟.改进自适应粒子群算法在WSN覆盖优化中的应用[J].传感技术学报,2016,29(4):559-565.DOI:10.3969/j.issn.1004-1699.2016.04.016.
[13]ZHANG Bin,MAO Jianlin,LI Haiping.A hybrid algorithm for sensing coverage problem in wireless sensor networks[C]//2011IEEE International Conference on Cyber Technology in Automation,Control,and Intelligent Systems.Piscataway,NJ:IEEE Press,2011:162-165.DOI:10.1109/CYBER.2011.6011785.
[14]丁旭,吴晓蓓,黄成.基于改进粒子群算法和特征点集的无线传感器网络覆盖问题研究[J].电子学报,2016,44(4):967-973.DOI:10.3969/j.issn.0372-2112.2016.04.030.
[15]潘峰,周倩,李位星,等.标准粒子群优化算法的马尔科夫链分析[J].自动化学报,2013,39(4),381-389.DOI:10.3724/SP.J.1004.2013.00381.
[16]刘志雄,梁华.粒子群算法中随机数参数的设置与实验分析[J].控制理论与应用,2010,27(11):1489-1496.DOI:10.7641/j.issn.1000-8152.2010.11.CCTA091682.
[17]KHAN S U,YANG Shiyou,WANG Luyu,et al.A modified particle swarm optimization algorithm for global optimizations of inverse problems[J].IEEE Transactions on Magnetics,2016,52(3):7000804.DOI:10.1109/TMAG.2015.2487678.
[18]王迪,吴鑫强,王振浩.基于改进遗传算法的含分布式电源配电网故障定位[J].东北电力大学学报,2016,36(1):1-7.DOI:10.3969/j.issn.1005-2992.2016.01.001.
[19]肖海林,任婵婵,聂在平,等.基于线性权重粒子群优化算法的多基站协作波束成型[J].电子科技大学学报,2015,44(5):663-667.DOI:10.3969/j.issn.1001-0548.2015.05.004.