<abstract> <p>Traditional back propagation neural networks (BPNNs) for ultrawideband (UWB) indoor localization can effectively improve localization accuracy, although there is high likelihood of becoming trapped in nearby minima. To solve this problem, the random weights and thresholds of the BPNN are optimized using the Harris Hawks optimization algorithm (HHO) to obtain the optimal global solution to enhance the UWB indoor positioning accuracy and NLOS resistance. The results show that the predicted trajectory of the HHO and BPNN hybrid algorithm (HHO-BP) matches the actual position in the two-dimensional localization scenario with four base stations; the optimized average positioning error is effectively reduced in both indoor LOS and NLOS environments. In the LOS environment, the total mean error of the traditional BPNN algorithm is 6.52 cm, which is 26.99% better than the UWB measurement error; in the NLOS environment, the total mean error of the conventional BPNN is 14.82 cm, which is 50.08% better than the UWB measurement error. The HHO–BP algorithm is further optimized on this basis, and the total mean error in the LOS environment is 4.50 cm, which is 22.57% better than the conventional BPNN algorithm; in the NLOS environment, the total mean error is 9.56 cm, which is 17.54% better than the conventional BPNN algorithm. The experimental findings suggest that the approach has greater calibration accuracy and stability than BPNN, making it a viable choice for scenarios requiring high positional precision.</p> </abstract>

Summary The geometric skew model is widely used in image classification and image retrieval. In the traditional skew model, the geometric topology statistical method ignores the spatial information between geometric words and the classification object shape information, which leads to the distinguishing ability of image features. In this paper, we propose a geometric skew method with adaptive reasoning. Combined with significant region extraction and geometric topology, we not only can generate more representative geometric topologies but also can avoid complex background information and position change bands to a certain extent. First, the adaptive reasoning geometric skewness model is constructed on the obtained significant area by carrying out the significant region extraction of the training image. Second, in order to describe the characteristics of the image more accurately, it can resist the changing position and background information. The method uses the geometric topology strategy and the polygon segmentation method to integrate global information and local information. Through the simulation experiment and compared with the traditional skewness model and other models, the results show that the method proposed in this paper obtains higher intelligence image classification accuracy.

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The Harris hawks optimization (HHO) algorithm is a new swarm-based natural heuristic algorithm that has previously shown excellent performance. However, HHO still has some shortcomings, which are premature convergence and falling into local optima due to an imbalance of the exploration and exploitation capabilities. To overcome these shortcomings, a new HHO variant algorithm based on a chaotic sequence and an opposite elite learning mechanism (HHO-CS-OELM) is proposed in this paper. The chaotic sequence can improve the global search ability of the HHO algorithm due to enhancing the diversity of the population, and the opposite elite learning can enhance the local search ability of the HHO algorithm by maintaining the optimal individual. Meanwhile, it also overcomes the shortcoming that the exploration cannot be carried out at the late iteration in the HHO algorithm and balances the exploration and exploitation capabilities of the HHO algorithm. The performance of the HHO-CS-OELM algorithm is verified by comparison with 14 optimization algorithms on 23 benchmark functions and an engineering problem. Experimental results show that the HHO-CS-OELM algorithm performs better than the state-of-the-art swarm intelligence optimization algorithms.