Multi-Objective Evolutionary Algorithm with Guiding Population for Three-Dimensional Terrain Coverage of Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are increasingly applied across various fields, but achieving effective three-dimensional terrain coverage in complex environments remains challenging due to signal obstruction and deployment optimization issues. This study proposes a Multi-Objective Evolutionary Algorithm with Guiding Population (MOEAGP) for 3D terrain coverage (WSNs-3DTC). The model introduces obstacle points and employs interpolation function fitting to improve signal blockage detection accuracy. Three objective functions are defined, namely non-coverage rate, coverage redundancy rate, and deployment-height cost, aiming to balance coverage performance and cost efficiency. The MOEAGP introduces a Guiding Population (GP), generated with uniform distribution based on terrain size and node number, to guide the main population’s evolution via a genetic algorithm and environmental selection. Experimental results demonstrate that MOEAGP outperforms seven comparison algorithms across different terrains and node quantities in terms of convergence, diversity, and optimization results. The results demonstrate the effectiveness of MOEAGP for the terrain-coverage optimization problem defined in this study, while practical deployment with device-specific sensing and network constraints remains to be investigated.