Clustering Routing Based on Improved Thermal Exchange Optimization Algorithm for WSN

Wireless sensor networks are widely used in areas such as monitoring of complex environments. Nodes consume energy due to sensing and transmission, and there is a hot-spot problem. Studies have shown that clustering is an effective method to solve the hot-spot problem. An improved thermal exchange optimization algorithm for selecting cluster heads to improve network QoS based on opposition-based learning and Levy flight mechanisms. By introducing a cluster equalization degree coefficient in the objective function, balanced network energy consumption and reduced network delays. Experimental results show that the proposed algorithm improves the average end-to-end delay of the network by 5%-10% compared to other classical algorithms, and improves the packet delivery rate, the number of packets received at the base station, energy consumption and network lifetime.