This study addresses the premature convergence issue of the Black-Winged Kite Algorithm (BKA) in high-dimensional optimization problems by proposing an enhanced hybrid algorithm (BKAPI). First of all, BKA provides dynamic global exploration through its hovering and dive attack strategies, while Particle Swarm Optimization (PSO) enhances local exploitation via its velocity-based search mechanism. Then, PSO enables efficient local refinement, and Differential Evolution (DE) introduces a differential mutation strategy to maintain population diversity and prevent premature convergence. Finally, the integration ensures a balanced exploration–exploitation trade-off, overcoming BKA’s sensitivity to parameter settings and insufficient local search capabilities. By combining these mechanisms, BKAPI achieves a robust balance, significantly improving convergence speed and computational accuracy. To validate its effectiveness, the performance of the enhanced hybrid algorithm is rigorously evaluated against seven other intelligent optimization algorithms using the CEC 2017 and CEC 2022 benchmark test functions. Experimental results demonstrate that the proposed integrated strategy surpasses other advanced algorithms, highlighting its superiority and strong application potential. Additionally, the algorithm’s practical utility is further confirmed through its successful application to three real-world engineering problems: welding beam design, the Himmelblau function, and visible light positioning, underscoring the effectiveness and versatility of the proposed approach.

The starfish optimization algorithm (SFOA) is a metaheuristic evolutionary intelligence algorithm with a great global search capability and strong adaptability. Although the SFOA has a good global search capability, it is not accurate enough in local search and converges slowly. To further enhance this convergence ability and global optimization ability, an enhanced starfish optimization algorithm (SFOAL) is proposed that combines sine chaotic mapping, t-distribution mutation, and logarithmic spiral reverse learning. The SFOAL can remarkably enhance both the global and local convergence capabilities of the algorithm, leading to a more rapid convergence speed and greater stability. In total, 23 benchmark functions and CEC2021 were used to test the development, search, and convergence capabilities of the SFOAL. The SFOAL was compared in detail with other algorithms. The experimental results demonstrated that the overall performance of the SFOAL was better than that of other algorithms, and the joint strategy could effectively balance the development and search capabilities to obtain stronger global and local optimization capabilities. For solving practical problems, the SFOAL was used to optimize the back propagation (BP) neural network to solve the ultra-wideband line-of-sight positioning problem. The results showed that the SFOAL-BP neural network had a smaller average position error compared to the random BP neural network and the SFOA-BP neural network, so it can be used to solve practical application problems.

This work demonstrates the dependence between magnetic properties and the thickness of NiFe thin films. More importantly, a quantitative study of the surface composition of NiFe thin film exposed to atmospheric conditions has been carried out employing angle-resolved X-ray photoelectron spectroscopy (ARXPS). In this study, we fabricated Ni₈₁Fe₁₉ (NiFe) thin films on Si (100) substrate using electron beam evaporation and investigated their surface morphologies, magnetic properties, and the thickness of the surface oxide layer. The coexistence of metallic and oxidized species on the surface are suggested by the depth profile of ARXPS spectra. The thickness of the oxidized species, including NiO, Ni(OH)₂, Fe₂O₃, and Fe₃O₄, are also estimated based on the ARXPS results. This work provides an effective approach to clarify the surface composition, as well as the thickness of the oxide layer of the thin films.