基于EGWOEO算法的三维无线传感网络覆盖优化

针对三维随机部署无线传感网络节点出现的覆盖率低和节点不均现象，以覆盖率为适应度函数，提出一种基于EGWOEO算法的三维无线传感网络覆盖优化算法。首先，采用Tent混沌映射初始化种群，以增加种群多样性；其次，利用反向学习策略，以增加全局搜索能力；之后，融入双曲正切高斯策略，加强算法寻优能力；然后，提出一种正余弦函数的非线性收敛因子，以平衡全局与局部搜索；最后，改进种群位置更新方程，加快算法的收敛速度与精度。将改进的EGWOEO算法应用于三维WSN覆盖优化中，仿真结果表明，与GWO、PSOGWO、LGWO算法相比，EGWOEO算法的三维WSN覆盖率平均增量分别为11.023%、10.662%和12.401%,改善了节点分布不均现象，提高了节点利用率。

Coverage optimization of 3D wireless sensor networks based on EGWOEO algorithm

Aiming at the phenomenon of low coverage and uneven nodes in three-dimensional wireless sensor networks with random deployment, taking the coverage as the fitness function, a coverage optimization algorithm for three-dimensional wireless sensor networks based on EGWOEO algorithm is proposed. Firstly, tent chaotic map is used to initialize the population to increase the diversity of the population. Secondly, reverse learning strategy is used to increase the global search ability. Thirdly, the hyperbolic tangent Gaussian strategy is integrated to strengthen the optimization ability of the algorithm. Then, a nonlinear convergence factor of sine and cosine function is proposed to balance the global and local search. Finally, the population position update equation is improved to speed up the convergence speed and accuracy of the algorithm. The improved EGWOEO algorithm is applied to 3D WSN coverage optimization, the simulation results show that compared with GWO, PSOGWO and LGWO algorithms, the average increment of 3D WSN coverage of EGWOEO algorithm is 11.023%, 10.662% and 12.401% respectively, which improves the uneven distribution of nodes and improves the utilization of nodes.