<p>Balanced energy utilization remains a critical problem in Wireless Sensor Networks (WSNs), where sensor nodes encounter power constraints and are often deployed in remote locations. Cluster-based routing is widely regarded as an effective technique to prolong network lifetime; however, its effectiveness depends heavily on the ongoing determination of optimal Cluster Heads (CHs). Conventional metaheuristic-based CH selection schemes typically exhibit early convergence and insufficient solution exploration, resulting in inefficient energy distribution and, consequently, degraded network performance. This article proposes a novel routing protocol, the Enhanced Archimedes Optimization Algorithm (EAOA), for energy-saving CH selection in a WSN. EAOA improves the standard Archimedes Optimization Algorithm (AOA) by applying a dimension-learning search mechanism and adaptive parameter adjustments via the Honey Badger Algorithm (HBA). The CH selection procedure is expressed as a multi-target optimization dilemma that accounts for residual energy, local density, distance from nodes to base stations, and previously selected CHs. EAOA is tailored to operate within this formulation by encoding candidate CHs as solution objects and optimizing them through iterative search dynamics. Experimental results prove that RA-EAOA outperforms comparative algorithms regarding network longevity, energy requirements, and packet delivery percentage.</p>