Energy-Efficient Cluster Head Selection Using Hybrid Sandpiper Competitive Swarm Optimization In Wireless Sensor Networks

Wireless Sensor Networks (WSNs) consist of numerous low-power sensor nodes deployed to monitor and collect environmental data. Energy conservation and improving the network life remain significant challenges due to limited battery capacity and high communication overhead. Clustering techniques, when combined with intelligent optimization algorithms, can significantly reduce energy consumption. However, conventional optimization methods often struggle to balance exploration and exploitation phases effectively. To address this limitation, this paper proposes a hybrid Sandpiper Optimization Algorithm–Competitive Swarm Optimization (SOA-CSO) technique for efficient Cluster Head (CH) selection. The hybrid algorithm leverages the global search capability of Sandpiper Optimization Algorithm and the competitive learning dynamics of Competitive Swarm Optimization to achieve optimal Cluster Head selection. Simulation results show that the proposed SOA-CSO achieves significant improvement in network life time, throughput, packet delivery ratio, load balance and network stability compared to the other algorithm.