刘向东, 宋欣, 王翠荣. 基于动态人工鱼群优化的WSN分簇算法[J]. 微电子学与计算机, 2011, 28(8): 43-46.
引用本文: 刘向东, 宋欣, 王翠荣. 基于动态人工鱼群优化的WSN分簇算法[J]. 微电子学与计算机, 2011, 28(8): 43-46.
shu
LIU Xiang-dong, SONG Xin, WANG Cui-rong. A Dynamic Artificial Fish Swarm Optimization Based Cluster Algorithm for Wireless Sensor Networks[J]. Microelectronics & Computer, 2011, 28(8): 43-46.
Citation: LIU Xiang-dong, SONG Xin, WANG Cui-rong. A Dynamic Artificial Fish Swarm Optimization Based Cluster Algorithm for Wireless Sensor Networks[J]. Microelectronics & Computer, 2011, 28(8): 43-46.
shu

基于动态人工鱼群优化的WSN分簇算法

A Dynamic Artificial Fish Swarm Optimization Based Cluster Algorithm for Wireless Sensor Networks

    摘要
  • 摘要: 将群智能优化算法引入无线传感器网络分簇路由协议的设计能有效地节约节点能量和提高分簇效率.针对基本人工鱼群算法在运算速度方面的不足,提出了一种基于动态人工鱼群优化的无线传感器网络分簇算法,算法为了同时具有较好的全局搜索和局部寻优能力,更快地得到最优分簇结果,在一次迭代进化中除了考虑人工鱼的觅食行为、聚群行为和追尾行为的寻优结果之外,还动态调整人工鱼的视野范围和前进步长两个重要参数.仿真结果证明,与LEACH,LEACH-C,AFSO算法比较,由于计算量的减少和计算速度的提高,此算法能有效地延长网络生命周期和减少能量消耗.

     

    Abstract: The swarm intelligence optimization algorithms are effective measures on saving node energy and improving cluster configuration for WSN routing protocol research.In order to decrease runtime of basic artificial fish swarm optimization,in this paper,a dynamic artificial fish swarm optimization based cluster algorithm for WSN was proposed.The algorithm achieve more appropriate cluster and better global/local optimization through dynamic adjusting visual and step parameters of artificial fish.The simulation results show that the total number of alive nodes is greater and the node energy dissipation is more decreasing than that is LEACH,LEACH-C and basic AFSO.

     

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