Abstract: In cluster-based sensor networks，energy can be conserved by combining redundant data from nearby sensors into cluster head nodes before forwarding the data to the destination.The lifespan of the whole network can also be expanded by the clustering of sensor nodes.But the existing algorithms are prone to lead nodes in clusters to die early due to ignoring the state of neighbors in the process of cluster-heads decision.Evolutionary algorithms have been applied successfully to various aspects.Particle Swarm Optimization（PSO） is one of evolutionary programming techniques.A new cluster-based algorithm using improved PSO is proposed to optimize clustering process.The election of cluster-heads need consider synthetically the state information of candidates and their neighbors.The simulation results show that this algorithm improves the WSN performance，and thus prolongs the network lifetime.

Key words: Improved Particle Swarm Optimization algorithm（IPSO）, Wireless Sensor Networks（WSN）, routing optimization, clustering

摘要： 在基于分簇的无线传感器网络中，网络是通过附近传感器节点在转发信息到目的节点前进行冗余数据的融合实现节能，从而延长了网络的生命周期。但现存的算法在选择簇首节点的过程中由于忽略了邻居节点的状态信息，容易导致簇内节点过早出现盲节点的现象。进化类算法已经成功应用于许多方面，微粒群算法就是其中之一。提出了一种基于改进型微粒群算法的无线传感器网络分簇路由算法来优化分簇过程。簇首节点的选取综合考虑候选节点和邻居节点的状态信息。仿真结果表明算法的性能得到了较好的改善，并延长了网络的生命周期。

关键词: 改进型微粒群算法, 无线传感器网络, 路由优化, 分簇