As node energy of wireless sensor networks (WSN) is limited and cannot be supplemented after exhaustion, clustering algorithm is frequently taken as an effective method to prolong the lifetime of WSN. However, the existing clustering algorithms have some drawbacks, either consuming excessive energy as a result of exchanging too much controlling information between nodes, or lacking a comprehensive perspective in terms of the balance among several conflicting objectives. In order to overcome these shortcomings, a novel combinatorial optimization-based clustering algorithm (COCA) for WSN is proposed in this paper. Different from the above mentioned algorithms which take clustering as a continuous optimization problem, COCA solves the clustering problem from the perspective of combinatorial optimization. Firstly, the clustering of WSN is abstracted into a combinatorial optimization problem. Then, the binary particle coding scheme of cluster head is proposed, which is based on the corresponding relationship between nodes and particle position vectors, and the fitness function is designed according to the parameters used in the process of cluster formation. Finally, the binary particle swarm optimization algorithm is applied to implement the clustering. COCA is validated under different scenarios compared with three other clustering algorithms. The simulation results show that COCA has better performance than its comparable algorithms.

中文翻译：

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基于组合优化的无线传感器网络聚类算法

由于无线传感器网络（WSN）的节点能量有限，用尽后无法补充，因此聚类算法经常被视为延长WSN寿命的有效方法。然而，现有的聚类算法具有一些缺点，要么由于在节点之间交换太多的控制信息而消耗过多的能量，要么就几个冲突的目标之间的平衡而言缺乏全面的观点。为了克服这些缺点，本文提出了一种新的基于组合优化的WSN聚类算法（COCA）。与上述将聚类作为连续优化问题的算法不同，COCA从组合优化的角度解决了聚类问题。首先，WSN的聚类被抽象为组合优化问题。然后，根据节点与粒子位置矢量之间的对应关系，提出了簇头的二进制粒子编码方案，并根据簇形成过程中使用的参数设计了适应度函数。最后，应用二元粒子群优化算法实现聚类。与其他三种聚类算法相比，COCA在不同的情况下得到了验证。仿真结果表明，与同类算法相比，COCA具有更好的性能。根据聚类形成过程中使用的参数设计适应度函数。最后，应用二元粒子群优化算法实现聚类。与其他三种聚类算法相比，COCA在不同的情况下得到了验证。仿真结果表明，与同类算法相比，COCA具有更好的性能。根据聚类形成过程中使用的参数设计适应度函数。最后，应用二元粒子群优化算法实现聚类。与其他三种聚类算法相比，COCA在不同的情况下得到了验证。仿真结果表明，与同类算法相比，COCA具有更好的性能。