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

Abstract

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.

Chimp Optimization Algorithm Contribution Support To Increases Energy Efficiency In UWASN

“…The second feature is their huge storage capacity, which includes keeping track of thousands of kilometres of routes. The last and most significant characteristic of buffaloes is their democratic nature [8]. If some buffaloes in the herd make competing cries, the buffaloes have an "election" to determine whether to leave or stay [9].…”

Hybrid Crow Search and Particle Swarm Algorithmic optimization based CH Selection method to extend Wireless Sensor Network operation

“…Further [20] have dealt with several goals including delay reduction as well as energy sustainability by implementing a clustering scheme based on inter-distance amid the CH as well as nodes. Optimization variables including distance, delay as well as energy are considered for efficient CH selection.…”

BEVGGC: Biogeography-Based Optimization Expert-VGG for Diagnosis COVID-19 via Chest X-ray Images

“…The migration stage also can be divided into two operations: immigration and emigration. When the algorithm performs the migration operation, it determines the immigration SIV and the emigration SIV according to the migration rate [18] replaces the value of the immigration SIV with the emigration SIV. When the algorithm performs the mutation operation, it randomly generates a value for each SIV and compares it with the mutation rate.…”