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.

This research presents a novel nature-inspired metaheuristic optimization algorithm, called the Narwhale Optimization Algorithm (NWOA). The algorithm draws inspiration from the foraging and prey-hunting strategies of narwhals, “unicorns of the sea”, particularly the use of their distinctive spiral tusks, which play significant roles in hunting, searching prey, navigation, echolocation, and complex social interaction. Particularly, the NWOA imitates the foraging strategies and techniques of narwhals when hunting for prey but focuses mainly on the cooperative and exploratory behavior shown during group hunting and in the use of their tusks in sensing and locating prey under the Arctic ice. These functions provide a strong assessment basis for investigating the algorithm’s prowess at balancing exploration and exploitation, convergence speed, and solution accuracy. The performance of the NWOA is evaluated on 30 benchmark test functions. A comparison study using the Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Perfumer Optimization Algorithm (POA), Candle Flame Optimization (CFO) Algorithm, Particle Swarm Optimization (PSO) Algorithm, and Genetic Algorithm (GA) validates the results. As evidenced in the experimental results, NWOA is capable of yielding competitive outcomes among these well-known optimizers, whereas in several instances. These results suggest that NWOA has proven to be an effective and robust optimization tool suitable for solving many different complex optimization problems from the real world.