Hybrid shuffled frog leaping and improved biogeography‐based optimization algorithm for energy stability and network lifetime maximization in wireless sensor networks

Summary Wireless sensor networks are significantly used for data sensing and aggregating dusts from a remote area environment in order to utilize them in a diversified number of engineering applications. The data transfer among the sensor nodes is attained through the inclusion of energy efficient routing protocols. These energy efficient routing necessitates optimal cluster head selection procedure for handling the challenge of energy consumption to extend the stability and lifetime in the sensor networks. The implementation of energy efficient routing is still complicated even when the process of clustering is enhanced through the cluster head selection. The majority of the existing cluster head selection schemes suffer from the issues of poor selection accuracy, increased computation, and duplicate nodes' selection. In this paper, hybrid shuffled frog leaping and improved biogeography‐based optimization algorithm (HSFLBOA) for optimal cluster head selection is proposed for resolving issues that are common in cluster head selection schemes. This proposed HSFLBOA used the objective function that used the parameters of node energy, data packet transmission delay, cluster traffic density, and internode distance in the cluster. The simulation results of the proposed HSFLBOA is determined to be significant in achieving superior throughput and network energy compared to benchmarked metaheuristic optimal cluster head schemes. Highlights➢Hybrid shuffled frog leaping and improved biogeography‐based optimization algorithm (HSFLBOA) for optimal cluster head selection is proposed for resolving issues that are common in cluster head selection schemes.➢HSFLBOA used the objective function that used the parameters of node energy, data packet transmission delay, cluster traffic density, and internode distance in the cluster.➢The results of the proposed HSFLBOA is significant in achieving superior throughput and network energy compared to benchmarked metaheuristic cluster head schemes.