基于改进混合粒子群优化算法的移动节点部署研究
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摘要
在概率感知优化模型下,将无线传感器网络(WSNs)的覆盖率和移动节点的能耗作为多目标优化函数,通过改进混合粒子群优化算法(IM-HPSO)不断迭代,调整移动节点的最优位置,控制网络覆盖率最大化,同时减小移动距离,使得能耗最小化。仿真结果表明:IM-HPSO算法在覆盖率的提高、能耗的减少、网络生命周期的延长方面优于其他算法。
In probabilistic perceptual optimization model,coverage rate of the wireless sensor networks( WSNs)and energy consumption of mobile node are used as multi-objective optimization function,and the optimal location of the mobile node is adjusted by improved hybrid particle swarm optimization( IM-HPSO) algorithm to control the network coverage rate maximization,at the same time,reducing moving distance,making the mobile energy consumption minimized. The simulation results show that the IM-HPSO algorithm outperforms the other algorithms in terms of coverage rate decrease of energy consumption and the extension of network lifetime.
In probabilistic perceptual optimization model,coverage rate of the wireless sensor networks( WSNs)and energy consumption of mobile node are used as multi-objective optimization function,and the optimal location of the mobile node is adjusted by improved hybrid particle swarm optimization( IM-HPSO) algorithm to control the network coverage rate maximization,at the same time,reducing moving distance,making the mobile energy consumption minimized. The simulation results show that the IM-HPSO algorithm outperforms the other algorithms in terms of coverage rate decrease of energy consumption and the extension of network lifetime.
引文
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[2]Mateska A,Gavrilovska L.WSNs coverage&connectivity improvement utilizing sensors mobility[C]∥Wireless Conference2011—Sustainable Wireless Technologies,2011:1-8.
[3]Liu J Z,Wang B L,Ao J Y,et al.An immune-swarm intelligence based algorithm for deterministic coverage problems of wireless sensor networks[J].Journal of Central South University,2012,19(11):3154-3161.
[4]周剑波,刘宏立,徐琨.一种结合粒子群和虚拟力的动态节点部署策略[J].计算机工程与应用,2016,52(10):118-123.
[5]袁曦,张曦煌.基于改进蝙蝠算法的无线传感器网络的移动节点部署[J].传感器与微系统,2016,35(3):144-146.
[6]王霞,陈洁.混合无线传感器网络节点覆盖优化[J].计算机仿真,2013,30(4):204-207.
[7]陈乐瑞,潘秋萍,李甜甜.基于遗传粒子群的微机运动网络优化研究[J].自动化技术与应用,2016,35(8):13-17.
[8]王建华,史明岳,王婷婷.基于量子粒子群算法的移动节点覆盖优化[J].微电子学与计算机,2012,29(6):96-99.
[9]艾兵,董明刚.基于高斯扰动和自然选择的改进粒子群优化算法[J].计算机应用,2016,36(3):687-691.
[10]谢世龙,周玉国,刘真.一种基于神经网络的粒子滤波算法设计[J].自动化技术与应用,2017,36(11):1-4.
[11]岳翀,熊芝,薛彬.基于模拟退火—粒子群算法的w MPS布局优化[J].光电工程,2016,43(7):67-73.
[12]黄炯,艾剑良,万婧.基于模拟退火粒子群算法的飞机气动参数辨识[J].复旦学报:自然科学版,2016,55(3):336-341.
[13]丁婷婷,高美凤.改进粒子滤波的无线传感器网络目标跟踪算法[J].传感器与微系统,2016,35(7):140-142.
[14]Luo C Y,Chen M Y,Li H.Advances in swarm and computational intelligence[M].Berlin Haidelberg:Springer International Publishing,2015:479-486.
[15]Tian J,Gao M,Ge G.Wireless sensor networks node optimal coverage based on improved genetic algorithm and binary ant colony algorithm[J].Eurasip Journal on Wireless Communications&Networking,2016,2016(1):1-11.