Improved Artificial Rabbit Optimization Algorithm Fused with Particle Swarm Optimization for Wireless Sensor Network Coverage Optimization

Aiming at the problem of low node coverage during node deployment in wireless sensor network (WSN), an improved artificial rabbit optimization algorithm incorporating particle swarm optimization (ARO-PSO) is proposed for network coverage optimization. ARO-PSO successfully combines the stochastic characteristics of ARO and the global characteristics of PSO. Firstly, to optimize the quality of the initial population, Sine chaos mapping is introduced to initialize the population; secondly, to better balance the exploration and exploitation, adaptive settings are made; finally, combined with the characteristics of the ARO energy factor, a population decreasing strategy is introduced to further accelerate the convergence speed of the algorithm. Experimental and analytical comparisons are made with ARO and PSO and 6 other excellent optimizers on 13 benchmark functions. The results show that ARO-PSO largely outperforms the original algorithm. Finally, ARO-PSO is applied to WSN coverage optimization experiments in 2D and 3D environments, and the proposed algorithm exhibits higher network coverage and improves the monitoring quality of the network compared to standard ARO and PSO and other state-of-the-art algorithms. The experimental results fully demonstrate the superiority of the ARO-PSO-based WSN node deployment optimization method.