Aiming to address the issue of suboptimal positioning accuracy in non-ranging node localization algorithms for wireless sensor networks, particularly in the context of multi-hop distance and average hop distance estimation methods that are limited in their capacity to rectify errors, resulting in the propagation of computational errors and consequent reduction in positioning accuracy, an improved black-winged kite algorithm-3D distance cetor-hop (IBKA-3DDV-Hop) localization algorithm is proposed. First, to reduce the hop quantization error, the number of hops between nodes is refined by using the multi-communication radius, and then the hop distance correction factor is introduced to compensate for the error of hop distance. Secondly, the optimal latin hypercube mechanism (OLHS) is employed to optimize the population initialization in the improved black-winged kite algorithm. This approach overcomes the limitations of random initialization and generates a reverse population through the Elite Reverse Learning strategy, which further enhances the quality of the initial population. In conclusion, the Levy flight strategy is integrated into the migration behavior of BKA. This integration serves to optimize and enhance the algorithm’s global search capability, thereby preventing the algorithm from attaining a local optimum. The simulation results demonstrate that, in comparison with the conventional 3DDV-Hop algorithm, multi-communication radius algorithm, GOOSE-3DDV-Hop algorithm, and WOA-3DDV-Hop algorithm, the proposed IBKA-3DDV-Hop localization algorithm reduces the normalized localization error by approximately 22%, 17%, 11%, and 6%, respectively. This improvement effectively enhances the accuracy of the non-ranging node localization algorithm.