Dendrobium polysaccharide is an important indicator for evaluating the quality of Dendrobium huoshanense. To improve the prediction accuracy of polysaccharide content, this study proposes an improved Artificial Lemming Algorithm (IALA) optimized BP neural network model. In IALA, a periodic mutation strategy and a fast hybrid opposition learning strategy (FHOBL) are introduced to enhance population diversity, improve global search ability, and avoid premature convergence. The proposed IALA was first evaluated on CEC2017 and CEC2020 benchmark functions. Experimental results show that IALA achieves better or competitive performance compared with seven other algorithms in terms of mean fitness, best fitness, and standard deviation. Statistical tests, including Wilcoxon rank-sum and Friedman tests, further verify the significant superiority and robustness of IALA. Then, IALA was used to optimize the initial weights and thresholds of BP neural networks for Dendrobium polysaccharide content prediction. The results show that IALA-BP achieves the best overall prediction performance, with an R² of 0.8731, RMSE of 2.1581, and MSE of 4.6683. Compared with standard BP and other optimized BP models, IALA-BP provides more accurate and stable prediction results. Therefore, the proposed IALA-BP model is effective for rapid prediction of Dendrobium polysaccharide content.

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.

To address the problems of the Electromagnetic Wave Propagation Algorithm (EMWPA)—insufficient initial-population coverage, an imbalance between exploration and exploitation, and a tendency to fall into local optima—in high-dimensional complex optimization problems, this paper proposes a boosted electromagnetic wave propagation optimization algorithm, BEMWPA. First, a cubic chaotic map is introduced in the population-initialization stage to enhance the uniformity of the initial-solution distribution and the search-space coverage. Second, nonlinear phase modulation is applied to the electric- and magnetic-field driving terms, and a differentiated probabilistic switching mechanism is constructed to improve the dynamic coordination between global exploration and local exploitation. Furthermore, a Beta-distribution opposition-based learning strategy is introduced to enhance the algorithm’s ability to escape local optima by generating high-quality opposite candidate solutions. To verify the effectiveness of the proposed algorithm, systematic comparative experiments are conducted on the CEC2017 benchmark function set, and BEMWPA is combined with rapidly-exploring random tree (RRT) and applied to path planning of a welding manipulator in complex multi-workpiece scenarios. For a three-dimensional welding scenario containing 12 workpieces, 12 closed weld seams, and multiple obstacle constraints, BEMWPA-RRT reduces the initial inter-seam transfer path length of RRT from 586.00 mm to 479.11 mm, representing a relative reduction of 18.24%, and the complete end-effector path length is reduced from 2974.00 mm to 2867.11 mm, representing a relative reduction of 3.59%. Meanwhile, the optimized transfer path length is only 1.59 mm longer than the obstacle-free ideal transfer length of 477.52 mm, indicating that the proposed method can approach the geometric lower bound of this scenario while satisfying the obstacle-avoidance constraints. Kinematic verification on a seven-degrees-of-freedom welding manipulator further shows that the optimized Cartesian-space path can be converted into a continuously executable joint-space trajectory, providing an effective method for offline welding path planning of complex multi-workpiece tasks.

The Newton Downhill Optimizer (NDO) provides a compact derivative-free search framework, but its random dimension mask, uniform treatment of individuals, and limited use of supplementary global-best candidates may restrict performance on high-dimensional problems. This study proposes an Enhanced Newton Downhill Optimizer (ENDO) that incorporates three mechanisms: an adaptive dimension mask, a DE/rand/2-based greedy mutation for inferior individuals, and dynamic-boundary opposition-based best guidance. ENDO was evaluated on the CEC2017 benchmark at 10, 30, 50, and 100 dimensions. The primary comparison involved nine representative optimizers, and an additional 100-dimensional comparison was conducted with six advanced optimizers. In the primary 100-dimensional tests, ENDO ranked first on 18 of 29 functions, remained among the top three on 26 functions, and achieved an average rank of 1.79. In the additional comparison, ENDO ranked third with an average rank of 3.52. Across the four dimensions, its average ranks were 1.79, 1.66, 1.38, and 1.79, indicating stable performance under changes in problem scale. ENDO was further applied to a 168-dimensional thermal–pumped-storage economic dispatch problem, where it achieved the lowest mean operating cost, the best average rank of 1.87, and a feasibility rate of 100% over 30 independent runs. Statistical analyses further supported the competitiveness of ENDO across the benchmark and engineering evaluations. These results show that ENDO improves the sustained search capability of NDO and provides competitive performance for complex high-dimensional and constrained optimization problems.