计算机工程 ›› 2019, Vol. 45 ›› Issue (9): 70-75.doi: 10.19678/j.issn.1000-3428.0051983

• 移动互联与通信技术 • 上一篇    下一篇

异构传感器网络中基于差分进化的调度算法

李明1,2, 胡江平2   

  1. 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
  • 收稿日期:2018-07-02 修回日期:2018-09-01 出版日期:2019-09-15 发布日期:2019-09-03
  • 作者简介:李明(1982-),男,副教授、博士,主研方向为传感器网络;胡江平,教授、博士。
  • 基金项目:
    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)。

Scheduling Algorithm Based on Differential Evolution in Heterogeneous Sensor Network

LI Ming1,2, HU Jiangping2   

  1. 1. Chongqing Engineering Laboratory for Detection, Control and Integrated System, Chongqing Technology and Business University, Chongqing 400067, China;
    2. School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
  • Received:2018-07-02 Revised:2018-09-01 Online:2019-09-15 Published:2019-09-03
  • Supported by:
    This work is supported by the Science and Technology Project of SGCC (No.5229CG15004T).

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。

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

Abstract: 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.

Key words: learning automata, Differential Evolution(DE) algorithm, directional sensor network, node scheduling, heterogeneous network

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