Abstract: To address the issues of uneven node energy consumption and optimal cluster head selection in clustering routing for WSNs,this paper proposes a Clone Reverse Learning Grey Wolf Optimizer-based Energy-Balanced Routing Protocol(CRLGWORP).This algorithm introduces a clone selection mechanism into the traditional grey wolf optimizer framework,enhancing population diversity by replicating high-quality individuals,and combines reverse learning strategies to expand the search space for solutions,effectively improving global optimization capabilities.An adaptive weighting function is designed with the objectives of maximizing the network’s average residual energy and minimizing the average distance from cluster heads to the base station.The weights are dynamically adjusted based on the network’s energy distribution to balance the optimization focus between energy efficiency and communication distance.In the cluster head election phase,nodes with high energy and proximity to the base station are prioritized.During the data transmission phase,a multi-hop gradient relay mechanism is employed to optimize communication paths,reducing energy consumption for long-distance transmissions.Experimental results demonstrate that,compared with LEACH,LEACH-C,HEED,FIGWO and HGWCSOA-OCHS algorithms,the proposed algorithm significantly extends the network lifespan and improves node energy balance.

Key words: Wireless sensor networks, Cloning, Reverse learning, Grey wolf optimizer, Energy balance, Dynamic weighting