WSN coverage optimization based on multi-strategy improved particle swarm optimization algorithm

To address the issues of uneven distribution and low coverage caused by random deployment of nodes in wireless sensor networks (WSN), a WSN coverage optimization method based on a multi-strategy improved particle swarm optimization algorithm (MIPSO) is proposed. Firstly, the Circle map is introduced to initialize the population, enhancing population diversity to improve the global search efficiency of the algorithm. Secondly, the population is divided into elite and ordinary subpopulations using adaptive classification coefficients, and nonlinear decreasing inertia weights along with a new search mechanism are introduced to balance local exploitation and global exploration capabilities. Finally, a Cauchy perturbation strategy combined with Lévy flights is employed to enhance the algorithm's ability to escape local optima. Numerical simulations on 11 benchmark functions validate that MIPSO achieves superior convergence accuracy and stability. Furthermore, the improved algorithm is applied to WSN coverage optimization, achieving coverage rates of 97.51% and 99.39% in two experimental scenarios, demonstrating the feasibility and superiority of MIPSO in solving WSN coverage optimization problems.