Stochastic ranking improved teaching-learning and adaptive grasshopper optimization algorithm-based clustering scheme for augmenting network lifetime in WSNs

In Wireless Sensor Networks (WSNs), Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission. Several clustering protocols were devised for extending network lifetime, but most of them failed in handling the problem of fixed clustering, static rounds, and inadequate Cluster Head (CH) selection criteria which consumes more energy. In this paper, Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm (SRITL-AGOA)-based Clustering Scheme for energy stabilization and extending network lifespan. This SRITL-AGOA selected CH depending on the weightage of factors such as node mobility degree, neighbour's density distance to sink, single-hop or multihop communication and Residual Energy (RE) that directly influences the energy consumption of sensor nodes. In specific, Grasshopper Optimization Algorithm (GOA) is improved through tangent-based nonlinear strategy for enhancing the ability of global optimization. On the other hand, stochastic ranking and violation constraint handling strategies are embedded into Teaching-Learning-based Optimization Algorithm (TLOA) for improving its exploitation tendencies. Then, SR and VCH improved TLOA is embedded into the exploitation phase of AGOA for selecting better CH by maintaining better balance amid exploration and exploitation. Simulation results confirmed that the proposed SRITL-AGOA improved throughput by 21.86%, network stability by 18.94%, load balancing by 16.14% with minimized energy depletion by 19.21%, compared to the competitive CH selection approaches.