在异构无线传感器网络的研究中,聚类是最常用的节能方法之一。然而,现有的分簇方法在应用于异构无线传感器网络时面临挑战,比如能量平衡,节点异质性,算法效率,还有更多.在这些挑战中,设计良好的集群方法可以延长节点寿命。簇头的有效选择对于实现最优聚类至关重要。在本文中,我们提出了一种用于簇头选择的增强型Pelican优化算法(EPOA-CHS)来解决这些问题并增强簇头选择以实现最佳聚类。该方法将Levy飞行过程与传统POA算法相结合,这不仅提高了算法的优化水平,同时也保证了最优簇头的选择。在种群初始化中采用逻辑正弦混沌映射方法,通过新的适应度函数选择合适的簇头。最后,我们利用MATLAB在100×100m2的配置面积内模拟了100个传感器节点。这些节点分为四个异构场景:m=0,α=0,m=0.1,α=2,m=0.2,α=3和m=0.3,α=1.5。我们进行了四个方面的验证:总剩余能量,网络生存时间,幸存节点的数量,和网络吞吐量,在所有协议中。广泛的实验研究最终表明,EPOA-CHS方法优于SEP,DEEC,Z-SEP,和PSO-ECSM协议在这些方面。
In the research of heterogeneous wireless sensor networks, clustering is one of the most commonly used energy-saving methods. However, existing clustering methods face challenges when applied to heterogeneous wireless sensor networks, such as energy balance, node heterogeneity, algorithm efficiency, and more. Among these challenges, a well-designed clustering approach can lead to extended node lifetimes. Efficient selection of cluster heads is crucial for achieving optimal clustering. In this paper, we propose an Enhanced Pelican Optimization Algorithm for Cluster Head Selection (EPOA-CHS) to address these issues and enhance cluster head selection for optimal clustering. This method combines the Levy flight process with the traditional POA algorithm, which not only improves the optimization level of the algorithm, but also ensures the selection of the optimal cluster head. The logistic-sine chaotic mapping method is used in the population initialization, and the appropriate cluster head is selected through the new fitness function. Finally, we utilized MATLAB to simulate 100 sensor nodes within a configured area of 100 × 100 m2. These nodes were categorized into four heterogeneous scenarios: m=0,α=0, m=0.1,α=2, m=0.2,α=3, and m=0.3,α=1.5. We conducted verification for four aspects: total residual energy, network survival time, number of surviving nodes, and network throughput, across all protocols. Extensive experimental research ultimately indicates that the EPOA-CHS method outperforms the SEP, DEEC, Z-SEP, and PSO-ECSM protocols in these aspects.