There has been a huge surge in the Internet of Things (IoT) applications in recent years. The sensor nodes in the IoT network generate data continuously that directly affects the longevity of the network. Even though the potential of IoT applications are immense, there are numerous challenges like security, privacy, load balancing, storage, heterogeneity of devices, and energy optimization that have to be addressed. Of those, the energy utilization of the network is of importance and has to be optimized. Several factors like residual energy, temperature, the load of Cluster Head (CH), number of alive nodes, and cost function affect the energy consumption of sensor nodes. In this paper, a hybrid Whale Optimization Algorithm-Moth Flame Optimization (MFO) is designed to select optimal CH, which in turn optimizes the aforementioned factors. The performance of the proposed work is then evaluated with existing algorithms with respect to the energy-specific factors. The results obtained prove that the proposed method outperforms existing approaches. © 2020 Elsevier B.V.