In the field of ultra-wideband (UWB) indoor localization, traditional backpropagation neural networks (BPNNs) are limited by their susceptibility to local minima, which restricts their ability to achieve global optimization. To overcome this challenge, this paper proposes a novel hybrid algorithm, termed ARO-BP, which integrates the Artificial Rabbit Optimization (ARO) algorithm with a BPNN. The ARO algorithm optimizes the initial weights and thresholds of the BPNN, enabling the model to escape local optima and converge to a global solution. Experiments were conducted in both line-of-sight (LOS) and non-line-of-sight (NLOS) environments using a four-base-station configuration. The results demonstrate that the ARO-BP algorithm significantly outperforms traditional BPNNs. In LOS conditions, the ARO-BP model achieves a localization error of 6.29 cm, representing a 49.48% reduction compared to the 12.45 cm error of the standard BPNN. In NLOS scenarios, the error is further reduced to 9.86 cm (a 46.96% improvement over the 18.59 cm error of the baseline model). Additionally, in dynamic motion scenarios, the trajectory predicted by ARO-BP closely aligns with the ground truth, demonstrating superior stability. These findings validate the robustness and precision of the proposed algorithm, highlighting its potential for real-world applications in complex indoor environments.

An improved black-winged kite algorithm with multiple strategies (BKAIM) is proposed in this paper to address two critical limitations in the original black-winged kite optimization algorithm (BKA): the restricted search capability caused by the low-quality initial population and the reduced population diversity resulting from blind following behavior during the migration phase. Our enhancement implements three strategic modifications across different algorithm stages. During initialization, an opposition-based learning strategy was incorporated to generate a higher-quality initial population. For the migration phase, a differential mutation strategy was integrated to facilitate information exchange among population members, mitigate the tendency of blind leader-following behavior, enhance convergence precision, and achieve an optimal balance between exploration and exploitation capabilities. Regarding boundary handling, the conventional absorption boundary method was replaced with a random boundary approach to increase population diversity and subsequently improve the algorithm’s search capabilities. Comprehensive testing was conducted on four benchmark function sets (CEC2017, CEC2019, CEC2021, and CEC2022) to validate the effectiveness of the improved algorithm. Detailed convergence analysis and Wilcoxon rank-sum test comparisons with other algorithms demonstrated BKAIM’s superior convergence performance and robustness. Furthermore, the support vector machine (SVM) model was optimized by BKAIM for grade identification of Dendrobium huoshanense based on near-infrared spectral data, thereby confirming its effectiveness in practical applications.

Adaptive Reinforced Gray Langur Optimization (ARGLO), an enhanced variant of the Gray Langurs Optimizer, is developed for high-dimensional, multimodal, and nonlinear search landscapes susceptible to local trapping. Although the original GLO performs multi-population cooperative search by simulating the social structures of gray langurs, it still suffers from uneven random initialization, insufficient adaptive population partitioning, weak local perturbation, and premature convergence. ARGLO incorporates three strategies: good point set-based oppositional and quasi-oppositional learning initialization, hierarchical equilibrium adaptive population partitioning, and elite-guided hybrid mutation. Collectively, these mechanisms generate a higher-quality starting population, coordinate global search with local refinement, and reduce the risk of entrapment in suboptimal regions. Evidence from component-wise experiments together with the CEC test suite indicates that ARGLO delivers higher solution precision, steadier convergence, as well as more consistent performance, especially as dimensionality increases. Moreover, ARGLO is applied to near-infrared spectral feature selection and SVR parameter optimization for polysaccharide content prediction in Dendrobium huoshanense. Compared with unoptimized SVR, ARGLO-SVR reduces RMSE by 35.35% and improves R² by 21.92%; compared with GLO-SVR, it reduces RMSE by 6.05% and improves R² by 2.30%. These results demonstrate the effectiveness and application potential of ARGLO in complex optimization and rapid nondestructive quality detection of traditional Chinese medicinal materials.