改进平衡优化器算法的WSN覆盖优化

针对无线传感器网络在节点部署过程中存在节点覆盖空白及重叠覆盖的问题，提出一种改进平衡优化器算法（IEO）的网络覆盖优化。首先，利用环绕反向学习提高初始化种群质量，增强算法的优化能力；其次，引入动态正余弦因子进一步平衡全局搜索与局部开发能力，促使粒子种群对搜索空间中进行广泛搜索和深度挖掘；最后，通过在浓度更新阶段加入Circle混沌映射增加种群多样性，提高算法逃离局部最优的能力。实验结果表明，将IEO算法应用于WSN的覆盖优化实验中，与标准平衡优化器算法及其他改进算法相比，有效降低部署成本，表现出更高的网络覆盖率，改善网络的监测质量。

Improved equilibrium optimizer algorithm for WSN coverage optimization

Aiming at the problem of blank and overlapping coverage in node deployment of wireless sensor network, this paper proposed a network coverage optimization based on improved equilibrium optimizer(IEO) algorithm. Firstly, it used surround reverse learning to improve the quality of initial population and enhance the optimization ability of the algorithm. Secondly, it introduced dynamic sine and cosine factors to further balance global search and local development ability, so that particle population could search extensively and dig deeply in the search space. Finally, it added Circle chaos map in the concentration updating stage to increase the diversity of population and improve the algorithm''s ability to escape local optimum. The experimental results show that the IEO algorithm applied to the coverage optimization experiment of WSN can effectively reduce the deployment cost, show higher network coverage and improve the monitoring quality of the network compared with the standard balance optimizer algorithm and other improved algorithms.