ad hoc网络中基于离散粒子群优化的权重分簇算法

Novel Weighted Clustering Algorithm in Mobile Ad Hoc Networks Using Discrete Particle Swarm Optimization (DPSOWCA)

针对移动性较强的adhoc网络,提出了一种基于离散粒子群优化的权重分簇算法。该算法通过引入节点局部密度,仅利用节点局部拓扑信息来计算权重,然后通过离散粒子群优化簇头选举。在保证网络负载平衡的情况下,最大限度确保网络稳定性。仿真试验表明,与典型的权重分簇算法WCA比较,该算法提高了移动adhoc网络的稳定性,同时网络生命周期平均提高了88.28%。

Node mobility is the fundamental characteristic of ad hoc networks. A novel Weighted Clustering Algorithm in Mobile Ad Hoc Networks using Discrete Particle Swarm Optimization （DPSOWCA） was proposed. The local density of nodes was introduced to calculate weights of nodes, which only needed to know the local information of the node. And then the discrete particles swarm was used to optimize the selection cluster heads. In the case of ensuring the load balance of the network, the algorithm could maximize the network stability. The results of analysis and simulation indicate that it provides the load balance of cluster heads and the stability of the network. Compared with WCA, the DPSOWCA improves energy consumption so that the network lifetime was prolonged about 88.28%.