基于BBO优化K-means算法的WSN分簇路由算法

Clustering routing algorithm for WSN based on BBO optimized K-means

针对无线传感器网络中传感器节点能量有限、网络生存期短的问题,提出一种基于生物地理学算法优化K-means的无线传感器网络分簇路由算法BBOK-GA。成簇阶段,通过生物地理学优化算法改进K-means算法,避免求解时陷入局部最优。根据能量因子和距离因子设计了新的适应度函数选举最优簇首,完成分簇任务。数据传输阶段,则利用遗传算法为簇首节点搜寻到基站的最佳数据传输路径。仿真结果表明,相较于LEACH、LEACH-C、K-GA等算法,BBOK-GA降低了网络能耗,提高了网络吞吐量,延长了网络生存周期。

Aimed at the problems of limited energy and short network lifetime in wireless sensor network,BBOK-GA based on biogeographic algorithm optimization K-means was proposed.In wireless sensor networks,cluster head selection and routing are two well-known optimization problems.In the clustering stage,biogeographic algorithm optimization K-means was firstly used to prevent K-means from falling into the local optimum.According to the energy factor and distance factor,a new fitness function was designed to select optimal cluster heads and complete the clustering.After tHSI process,And genetic algorithm was used to search the optimal routing path towards base station for cluster heads.The simulation results indicate that BBOK-GA reduces the network energy consumption,increases increses the network throughput and extends the network life time compared to LEACH,LEACH-C,and K-GA.