北京航空航天大学学报 ›› 2010, Vol. 36 ›› Issue (3): 373-377.

• 论文 • 上一篇    

基于混合人工鱼群算法的传感器网络优化

廖灿星1, 张 平1, 李行善1, 张 岩2   

  1. 1. 北京航空航天大学 自动化科学与电气工程学院, 北京 100191;
    2. 中国人民解放军95661部队,重庆 400030
  • 收稿日期:2009-07-23 出版日期:2010-03-31 发布日期:2010-09-13
  • 作者简介:廖灿星(1976-),男,重庆大足人,博士生,liaocanxing@163.com.

Optimal deployment in sensor networks based on hybrid artificial fish school algorithm

Liao Canxing1, Zhang Ping1, Li Xingshan1, Zhang Yan2   

  1. 1. School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China;
    2. Unit 95661 of PLA, Chongqing 400030, China
  • Received:2009-07-23 Online:2010-03-31 Published:2010-09-13

摘要: 针对传感器网络节点优化的问题,提出一种混合人工鱼群算法.该算法在人工鱼群算法优化的末段引入模式搜索法,以人工鱼搜索到的最优解作为模式搜索法的初始解,利用模式搜索法的单调搜索特性,将解引向全局极值.新算法保留了人工鱼群算法全局搜索能力强、寻优速度快的特点,使寻优精度得到了提高.仿真实验表明:混合人工鱼群算法能够有效地优化传感器网络节点部署,提高覆盖率.

Abstract: A hybrid artificial fish school algorithm was presented for optimal nodes deployment of sensor networks. The hybrid artificial fish school algorithm included two phases. In speed priority phase, a suboptimal solution in the neighborhood of optimum solution was found rapidly by using the artificial fish school algorithm. In accuracy priority phase, taking the suboptimal solution as its initial solution and by using its monotonic convergence of the pattern search method, the solution to global extremum was led to. The merits of global search and rapid optimization of the artificial fish school algorithm were retained, and the search accuracy was improved. Node locations were optimized by artificial fish school algorithm, hybrid artificial fish school algorithm and particle swarm optimization in computer simulation for area coverage problem using the probabilistic detection model. Simulation results show that hybrid artificial fish school algorithm can effectively optimize the nodes deployment of sensor networks to improve coverage.

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