The sensor nodes localization is very advantageous in wireless sensor network (WSN). This allows effective data transfer between the sensor node networks. Therefore, it saves energy and prolongs network life. Here, a hybrid FWCELM‐CPO method is proposed for solving the sensor node localization problem in WSN. The proposed hybrid method is executed in MATLAB, and the performance is analyzed with different existing algorithms like centroid, fuzzy centroid, and ELM. The simulation results show that the proposed FWCELM‐CPO method attains the higher detection rate of 14.117%, 5.435%, and 11.494%, higher segmentation accuracy of 9.556%, 26.41%, and 16%, lower execution time of 66.667%, 75%, and 70.37%, higher segmented region of 65.957%, 20%, and 44.444%, and higher precision of 34.72%, 18.29%, and 8.78% compared to the existing algorithms. The simulation results demonstrate that the proposed method can be able to find the optimal global solutions efficiently with accurately.

中文翻译：

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具有乌鸦粒子优化方法的混合模糊加权质心和极限学习机，用于解决无线传感器网络中的定位问题

传感器节点的定位在无线传感器网络（WSN）中非常有利。这允许在传感器节点网络之间进行有效的数据传输。因此，它可以节省能源并延长网络寿命。这里，提出了一种混合的FWCELM-CPO方法来解决WSN中的传感器节点定位问题。提出的混合方法在MATLAB中执行，并使用质心，模糊质心和ELM等现有算法对性能进行了分析。仿真结果表明，所提出的FWCELM-CPO方法具有较高的检测率，分别为14.117％，5.435％和11.494％，较高的分割精度为9.556％，26.41％和16％，执行时间较低，分别为66.667％，75％ ，和70.37％，更高的分割区域为65.957％，20％和44.444％，与现有算法相比，精度更高，分别为34.72％，18.29％和8.78％。