基于生物地理学优化的分簇算法

Clustering algorithm based on biogeography-based optimization

为提高WSN的能量效率和网络吞吐量,提出一种基于生物地理学优化的分簇算法(CABBO)。从3个方面提高生物地理学优化(BBO)在WSN分簇的适应性。通过综合节点剩余能量、邻居节点个数、与基站距离因素,选取综合值大的节点优化初始栖息地种群质量,提高算法收敛速度;采用余弦迁移模型提高算法的探索能力;通过考虑多个性能指标定义目标函数,实现网络的有效连接和负载均衡。实验结果表明,该算法在网络生命周期、能量效率、吞吐量和负载均衡等方面具有较优性能表现。

To improve the energy efficiency and network throughput of WSN,a clustering algorithm based on biogeography-based optimization(CABBO)was proposed.The adaptability of biogeography-based optimization(BBO)in WSN clustering was improved from three aspects.The quality of initial habitat populations was optimized by selecting nodes with large merged values of node residual energy,number of neighboring nodes,and distance from the base station.A cosine migration model was introduced to boost the exploratory capabilities of the algorithm.The objective function was defined by considering multiple performance metrics to achieve effective connectivity and load balancing of the network.Experimental results show that the proposed algorithm has good performance in terms of the network life cycle,energy efficiency,throughput,and load-balanced.