The target of wireless sensor networks is to gather data regarding certain phenomena. This is why they are meant to last as long as possible. An efficient strategy to extend the wireless sensor network lifetime is to organize sensors hierarchically into clusters and, for each cluster, select one of the sensors as the cluster head. The task of the cluster head is to collect data from the cluster members and retransmit it to the base station across the network. By means of cluster organization, the process of acquiring data is enhanced and, consequently, the lifetime of the network is increased. Nevertheless, in addition to data collection and transmission, every cluster head performs additional tasks. Thus, they spend their energy quicker than the cluster members. This is the reason why cluster head selection is a multi-objective optimization problem. In this paper, we consider the three objectives, distance, delay, and residual energy, and apply three multi-objective evolutionary algorithms, specifically NSGA-II, SMS-EMOA, and MOEA/D, for studying the problem by means of solving several instances. We analyze the conflict between the objectives, present a proper multi-objective performance comparison of the algorithms, and investigate the efficiency of the found solutions regarding network energy consumption.

无线传感器网络的目标是收集有关某些现象的数据。这就是为什么它们应该持续尽可能长的时间。延长无线传感器网络寿命的一种有效策略是将传感器分层组织成簇，并为每个簇选择一个传感器作为簇头。簇头的任务是从簇成员那里收集数据，并通过网络重传给基站。通过集群组织，增强了获取数据的过程，从而增加了网络的生命周期。然而，除了数据收集和传输之外，每个簇头还执行额外的任务。因此，他们比集群成员更快地消耗精力。这就是为什么簇头选择是一个多目标优化问题的原因。在本文中，我们考虑三个目标，距离、延迟和剩余能量，并应用三种多目标进化算法，特别是 NSGA-II、SMS-EMOA 和 MOEA/D，通过解决几个问题来研究这个问题。实例。我们分析了目标之间的冲突，对算法进行了适当的多目标性能比较，并研究了所找到的网络能耗解决方案的效率。