异构传感器网络中基于差分进化的调度算法  

Scheduling Algorithm Based on Differential Evolution in Heterogeneous Sensor Network

作　　者：李明[1,2] 胡江平 LI Ming;HU Jiangping(Chongqing Engineering Laboratory for Detection,Control and Integrated System,Chongqing Technology and Business University,Chongqing 400067,China;School of Automation Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China)

机构地区：[1]重庆工商大学重庆市检测控制集成系统工程实验室,重庆400067 [2]电子科技大学自动化工程学院,成都611731

出　　处：《计算机工程》2019年第9期70-75,共6页Computer Engineering

基　　金：重庆市检测控制集成系统工程实验室开放课题(611315002);重庆市教委科学技术研究项目(KJ1600627,KJZH17124);重庆市社会科学规划项目(2017YBGL142);重庆工商大学科研平台开放课题(KFJJ2017048)

摘　　要：为提高异构有向传感器网络的节点调度效率,基于学习自动机提出一种参数自适应的差分进化算法。将节点调度问题转化为集合覆盖问题,利用学习自动机与环境的交互实现差分算法控制参数的自适应选择,同时采用自适应的变异策略增强算法解决集合覆盖问题时的寻优能力。仿真结果表明,相比原始差分进化算法,该算法拓展了参数自适应性,优化能力更强,并且能够延长异构有向传感器网络的生存时间。In order to improve the efficiency of node scheduling in Heterogeneous Directional Sensor Network(HDSN), a Differential Evolution(DE) algorithm with parameter self-adaption based on learning automata is proposed.It transforms the node scheduling problem into the set covering problem,and realizes adaptive selection of differential algorithm’s control parameters by using interaction between learning automata and environment.Meanwhile,it adopts the adaptive mutation strategy to enhance the optimization ability of the algorithm in solving the set coverage problem.Simulation results show that,compared with original DE algorithm,the proposed algorithm expands the parameter’s self-adaptability and has stronger optimization ability.Meanwhile,it can prolong the lifespan of HDSN.

关 键 词：学习自动机 差分进化算法 有向传感器网络 节点调度 异构网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]