基于自适应惯性权重的混沌粒子群优化无线传感器网络成簇算法

为了解决簇头选举过程中多因素冲突问题，以优化簇头选举和延长网络生命周期为目标，提出一种基于自适应惯性权重混沌粒子群优化（AWCPSO）的分簇算法.该算法在簇头竞选过程中，考虑了节点剩余能量、与基站的距离以及该节点担任簇头的概率，通过自适应惯性权重的混沌粒子群算法优化簇头的选举，并将通信范围内的节点作为其簇成员.簇头数目的选择满足最优簇头个数，从而进一步提高了网络的能量使用效率.仿真结果表明，与SEP和DEEC算法相比，本文算法能够更有效的节省能量，网络稳定周期分别延长62.31%和16.45%，同样有效的均衡网络能量消耗，延长了网络生命周期.

Clustering Protocol for Wireless Sensor Networks Based on Inertia Weight Chaos-PSO Optimization

In order to solve the multi-factor conflict problem in cluster head election process, a clustering algorithm based on adaptive inertia weight chaotic particle swarm optimization (AWCPSO) is proposed to optimize the cluster head election and extend the network life cycle. This algorithm considers the residual energy of the nodes, the distance from the base station and the probability of the node as the cluster head during the cluster head election process. At the same time, it uses the adaptive inertia weight chaotic particle swarm algorithm to optimize the cluster head election, and elects the cluster members around the node communication range. The number of cluster heads can satisfy the optimal number of cluster heads, which further improves the energy efficiency of the network. The simulation results show that the proposed algorithm can save energy more effectively compared with the SEP and DEEC algorithm, and the stability and lifetime of the network can be improved by 62.31% and 16.45%, respectively.