QRC-PSO: A Quad-Module Ring-Competitive PSO Algorithm for Coverage Optimization in Wireless Sensor Networks

Abstract

Coverage optimization in WSNs is critical for disaster warning and industrial monitoring, but is challenging due to multi-modality and high-dimensionality. Particle swarm optimization (PSO) offers fast convergence and simple parameter tuning, yet suffers from premature convergence and parameter sensitivity in multi-peak problems. To address these issues, we propose a Quad-module Ring-Competitive PSO (QRC-PSO). It comprises four heterogeneous subgroups with distinct parameter configurations for global exploration, local exploitation, balanced search, and perturbation enhancement. Subgroups evolve independently but exchange elite particles via a ring-topology migration strategy: every 20 iterations, the best three particles of each subgroup move clockwise to the next subgroup and replace its three worst ones, enabling high-quality solution diffusion while preserving diversity. Simulations on a 100 m × 100 m field with 20 and 30 nodes show that QRC-PSO achieves coverage rates of 84.69% and 98.62%, outperforming GA, standard PSO, APSO, LPSO, ALPSO, GWO, and DE. Tests on a 500 m × 500 m area with 500 and 750 nodes further confirm its superiority. These results demonstrate that the proposed subgroup structure and competitive mechanism effectively overcome traditional PSO weaknesses, making QRC-PSO an efficient and reliable solution for WSN coverage optimization.