Adaptive Enhanced Grey Wolf Optimizer for Efficient Cluster Head Selection and Network Lifetime Maximization in Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), survivability is a crucial issue that is greatly impacted by energy efficiency. Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints in WSNs. This paper presents an Adaptive Enhanced Grey Wolf Optimizer (AEGWO) for energy-efficient cluster head (CH) selection that mitigates the exploration–exploitation imbalance, preserves population diversity, and avoids premature convergence inherent in baseline GWO. The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation, a hybrid velocity-momentum update based on the dynamics of PSO, and an intelligent mutation operator to maintain the diversity of the population. The search is guided by a multi-objective fitness, which aims at maximizing the residual energy, equal distribution of CH, minimizing the intra-cluster distance, desirable proximity to sinks, and enhancing the coverage. Simulations on 100 nodes homogeneous WSN Tested the proposed AEGWO under the same conditions with LEACH, GWO, IGWO, PSO, WOA, and GA, AEGWO significantly increases stability and lifetime compared to LEACH and other tested algorithms; it has the best first, half, and last node dead, and higher residual energy and smaller communication overhead. The findings prove that AEGWO provides sustainable energy management and better lifetime extension, which makes it a robust, flexible clustering protocol of large-scale WSNs.