改进杂草算法求解WSN节点分布优化问题
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摘要
对改进杂草算法性能及其在无线传感器网络节点分布优化问题中的应用进行研究。在保证节点相互连通的前提下,建立无线传感器网络对目标区域覆盖的数学模型,并将节点分布优化问题转换为求解函数最大值问题;通过杂草算法优越的寻优能力来实现网路节点的最优分布,在此基础上,引入立方映射混沌算子来提高算法的局部搜索能力,利用高斯变异算子来增强种群的多样性;最后,通过标准函数测试与无线网络覆盖优化仿真对该算法进行验证。仿真结果表明:该算法具有收敛速度快、鲁棒性好、数据开采能力强的优点,能有效解决无线传感器网络节点分布优化问题。
The performance of improved invasive weed optimization algorithm( IIWO) and its application for coverage optimization in wireless sensor networks were discussed.On the premise that connectivity among nodes was guaranteed,a mathematical model was established to achieve the coverage of objective area with wireless sensor networks.This problem was transformed into function optimization based on this algorithm.Then,the invasive weed optimization algorithm was used to search the optimal deployment with the strong search performance.The cubic mapping chaotic operator was introduced to enhance the ability of local search and robustness,and the Gauss mutation operator was used to keep the diversity of population.Lastly,the proposed algorithm was verified through the numerical benchmark functions and coverage simulation.All the results show that the proposed algorithm has fast convergence speed,nice robustness and strong ability of data mining.Hence,it has the ability to solve the problem of deployment problem in wireless sensor networks.
The performance of improved invasive weed optimization algorithm( IIWO) and its application for coverage optimization in wireless sensor networks were discussed.On the premise that connectivity among nodes was guaranteed,a mathematical model was established to achieve the coverage of objective area with wireless sensor networks.This problem was transformed into function optimization based on this algorithm.Then,the invasive weed optimization algorithm was used to search the optimal deployment with the strong search performance.The cubic mapping chaotic operator was introduced to enhance the ability of local search and robustness,and the Gauss mutation operator was used to keep the diversity of population.Lastly,the proposed algorithm was verified through the numerical benchmark functions and coverage simulation.All the results show that the proposed algorithm has fast convergence speed,nice robustness and strong ability of data mining.Hence,it has the ability to solve the problem of deployment problem in wireless sensor networks.
引文
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[13]CHEN G,WU X,ZHU X,et al. Efficient String Matching with Wildcards and Length Constraints[J]. Knowledge and Information Systems,2006,10(4):399-419.
[14]黄霞,叶春明,曹磊.一种混沌变异的入侵杂草优化算法及性能仿真[J].系统仿真学报,2016,28(8):1732-1739.HUANG X,YE C M,CAO L. Invasive Weed Optimization Algorithm Combined with Chaotic Mutation and Analysis of Its Property[J]. Journal of System Simulation,2016,28(8):1732-1739.
[15]LEI L,SHIRU Q. Path Planning for Unmanned Air Vehicles Using an Improved Artificial Bee Colony Algorithm[C]//Control Conference,2012:2486-2491.
[2]LIU X Y,ZHU Y,KONG L,et al. CDC:Compressive Data Collection for Wireless Sensor Networks[J]. IEEE Transactions on Parallel and Distributed Systems,2015,26(8):2188-2197.
[3]HUSSAIN S,MATIN A W,ISLAM O. Genetic Algorithm for Hierarchical Wireless Sensor Networks[J]. Journal of Networks,2007,2(5):87-97.
[4]KUILA P,JANA P K. Energy Efficient Clustering and Routing Algorithms for Wireless Sensor Networks:Particle Swarm Optimization Approach[J]. Engineering Applications of Artificial Intelligence,2014,33:127-140.
[5] OZTURK C,KARABOGA D,GORKEMLI B. Probabilistic Dynamic Deployment of Wireless Sensor Networks by Artificial Bee Colony Algorithm[J]. Sensors,2011,11(6):6056-6065.
[6]熊伟丽,刘欣,陈敏芳,等.基于差分蜂群算法的无线传感器网络节点分布优化[J].控制工程,2014,21(6):1036-1040.XIONG W L,LIU X,CHEN M F,et al. Node Distribution Optimization in Wireless Sensor Networks Based on Differential Bee Colony Algorithm[J]. Control Engineering of China,2014,21(6):1036-1040.
[7]刘洲洲,王福豹,张克旺.基于改进萤火虫优化算法的WSN覆盖优化分析[J].传感技术学报,2013,26(5):675-682.LIU Z Z,WANG F B,ZHANG K W. Performance Analysis of Improved Glowworm Swarm Optimization Algorithm and the Application in Coverage Opimization of WSNs[J]. Chinese Journal of Sensors and Actuators,2013,26(5):675-682.
[8] LIN C,WU G,XIA F,et al. Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks[J]. Journal of Computer and System Sciences,2012,78(6):1686-1702.
[9]MEHRABIAN A R,LUCAS C. A Novel Numerical Optimization Algorithm Inspired from Weed Colonization[J]. Ecological Informatics,2006,1(4):355-366.
[10]OZTURK C,KARABOGA D,GORKEMLI B. Artificial Bee Colony Algorithm for Dynamic Deployment of Wireless Sensor Networks[J]. Turkish Journal of Electrical Engineering&Computer Sciences,2012,20(2):255-262.
[11]RANI D S,SUBRAHMANYAM N,SYDULU M. Multi-objective Invasive Weed Optimization:An Application to Optimal Network Reconfiguration in Radial Distribution Systems[J]. International Journal of Electrical Power&Energy Systems,2015,73(10):932-942.
[12]SARAVANAN B,VASUDEVAN E R,KOTHARI D P. Unit Commitment Problem Solution Using Invasive Weed Optimization Algorithm[J]. International Journal of Electrical Power&Energy Systems,2014,55:21-28.
[13]CHEN G,WU X,ZHU X,et al. Efficient String Matching with Wildcards and Length Constraints[J]. Knowledge and Information Systems,2006,10(4):399-419.
[14]黄霞,叶春明,曹磊.一种混沌变异的入侵杂草优化算法及性能仿真[J].系统仿真学报,2016,28(8):1732-1739.HUANG X,YE C M,CAO L. Invasive Weed Optimization Algorithm Combined with Chaotic Mutation and Analysis of Its Property[J]. Journal of System Simulation,2016,28(8):1732-1739.
[15]LEI L,SHIRU Q. Path Planning for Unmanned Air Vehicles Using an Improved Artificial Bee Colony Algorithm[C]//Control Conference,2012:2486-2491.