With the popularity of swarm intelligence algorithms, the positioning of nodes to be located in wireless sensor networks (WSNs) has received more and more attention. To overcome the disadvantage of large ranging error and low positioning accuracy caused by the positioning algorithm of the received signal strength indication (RSSI) ranging model, we use the RSSI modified by Gaussian to reduce the distance measurement error and introduce an improved whale optimization algorithm to optimize the location of the nodes to be positioned to improve the positioning accuracy. The experimental results show that the improved whale algorithm performs better than the whale optimization algorithm and other swarm intelligence algorithms under 20 different types of benchmark function tests. The positioning accuracy of the proposed location algorithm is better than that of the original RSSI algorithm, the hybrid exponential and polynomial particle swarm optimization (HPSO) positioning algorithms, the whale optimization, and the quasiaffine transformation evolutionary (WOA-QT) positioning algorithm. It can be concluded that the cluster intelligence algorithm has better advantages than the original RSSI in WSN node positioning, and the improved algorithm in this paper has more advantages than several other cluster intelligence algorithms, which can effectively solve the positioning requirements in practical applications.

中文翻译：

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基于改进鲸鱼优化的无线传感器网络节点定位算法

随着群体智能算法的普及，无线传感器网络（WSN）中节点的定位越来越受到关注。为克服接收信号强度指示（RSSI）测距模型的定位算法导致的测距误差大、定位精度低的缺点，我们使用高斯修正的RSSI来降低测距误差，并引入改进的鲸鱼优化算法，优化待定位节点的位置，提高定位精度。实验结果表明，改进后的鲸鱼算法在20种不同类型的基准函数测试下均优于鲸鱼优化算法和其他群体智能算法。所提出的定位算法的定位精度优于原始RSSI算法、混合指数多项式粒子群优化（HPSO）定位算法、鲸鱼优化和拟仿射变换进化（WOA-QT）定位算法。可以得出结论，集群智能算法在WSN节点定位方面比原来的RSSI具有更好的优势，并且本文改进的算法比其他几种集群智能算法具有更多的优势，可以有效解决实际应用中的定位需求。和准仿射变换进化（WOA-QT）定位算法。可以得出结论，集群智能算法在WSN节点定位方面比原来的RSSI具有更好的优势，并且本文改进的算法比其他几种集群智能算法具有更多的优势，可以有效解决实际应用中的定位需求。和准仿射变换进化（WOA-QT）定位算法。可以得出结论，集群智能算法在WSN节点定位方面比原来的RSSI具有更好的优势，并且本文改进的算法比其他几种集群智能算法具有更多的优势，可以有效解决实际应用中的定位需求。