An Energy-Saving Clustering Algorithm for Wireless Sensor Networks Based on Multi-Objective Walrus Optimization

Wireless sensors serve as a critical means of information perception and collection, profoundly influencing human life and production. In order to optimize the problem of excessive energy drain caused by the selection of cluster heads and the transmission of paths in the network, this study proposes an energy-efficient clustering–routing algorithm that combines K-means++ initialization with the multi-objective Chaotic Mapping Walrus Optimization Algorithm (CM-WaOA). The CM-WaOA employs chaotic mapping and Pareto front optimization to balance node residual energy, cluster-head-to-base-station distance, inter-cluster-head distance, and intra-cluster node count variance when selecting cluster heads. Subsequently, the Sparrow Search Algorithm (SSA) refines routing paths through adaptive population sizing and elite retention, thereby reducing transmission path loss. The simulation results over 1000 rounds demonstrate that the CM-WaOA surpasses LEACH, EEUC, CGWOA, and EBPT-CRA in terms of energy drain, node survival, and latency; it achieves the highest average residual energy, the fewest dead nodes, the most surviving nodes, and the shortest network delay. These findings confirm that the CM-WaOA can still maintain good energy utilization and low-latency characteristics under different sensor densities, effectively extending the network lifetime.