Implementation of Modified Grasshopper and Differential Evolution-based Optimization Scheme for Network Stability in WSN

In Wireless Sensor Networks (WSNs) an effectual scheme for selection of Cluster Head (CH) is vital for attaining improved co-operative data processing. Swarm-intelligent Meta-Heuristic (MH)-based methods for choice of CH are found to be efficient for designing energy-proficient methods which choose ideal CHs from nodes. In this article, Modified Grasshopper and Differential Evolution-based Optimization Algorithm (MGHDEOA) is proposed for achieving network stability in homogenous as well as heterogeneous networks. MGHDEOA incorporates a dynamic mechanism into Differential Evolution (DE) for enhancing global searching ability during optimization. The proposed scheme is designed for enhancing convergence efficacy and preserving diversity of population. It owns the proficiency of improving convergence speed as well as precision. Incorporation of Grasshopper Optimisation Algorithm (GOA) into DE avoids early convergence of the algorithm, as deviation among scaling entities persuades population to be arbitrarily disseminated, focusing on retaining population diversity. Simulation investigation of the proposed scheme is performed well in terms of network lifespan, convergence rate, computation time and communication cost when compared to standard mechanisms taken for examination.