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

Abstract

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 map** 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.

摘要

针对无线传感器网络 (WSN) 在节点部署过程中存在节点覆盖率较低的问题, 提出一种基于融合粒子群优化改进人工兔优化算法 (ARO-PSO) 的网络覆盖优化. ARO-PSO 成功地融合了人工兔优化的随机特性和粒子群优化的全局特性. 首先, 为了优化初始种群的质量引入了 Sine 混沌映射对种群进行初始化; 其次, 为了更好地**衡勘探和开发, 进行了适应性设置; 最后, 结合人工兔优化能量因子的特性, 引入了种群递减策略以进一步加快算法的收敛速度. 通过在 13 个基准函数上与人工兔优化和粒子群优化及其他6种优秀的优化器进行实验和分析比较. 结果表明: ARO-PSO 的性能很大程度上超过了原算法. 最后, 将 ARO-PSO 应用于 2D 和 3D 环境的无线传感器网络覆盖优化实验中, 与标准人工兔优化和粒子群优化及其他最新算法相比, 所提出的算法表现出了更高的网络覆盖率, 改善了网络的监测质量. 实验结果充分证明了基于 ARO-PSO 的无线传感器网络节点部署优化方法的优越性.