Wavefront correction based on African vulture optimization algorithm

Recent advancements in wavefront sensorless Adaptive Optics (AO) systems have attracted significant scholarly attention, primarily attributed to their free of wavefront detection, streamlined architectures, and enhanced functional capabilities. The control algorithm determines the correction performance of the wavefront sensorless AO system. Among the existing algorithms, certain algorithms demonstrate robust global convergence properties despite their relatively slow convergence rates. Some algorithms exhibit limited global optimization capabilities but converge more rapidly. Others achieve rapid convergence but require extensive preprocessing. The African Vulture Optimization Algorithm (AVOA) is introduced to control the wavefront sensorless AO system in this paper, which is inspired by the observation of the hunting and flight behaviors of African vulture populations. In the Matlab programming environment, an adaptive optics system based on AVOA is built, which mainly includes a 61-element deformable mirror (DM) element and an imaging sensor device. Based on this AO system model, we studied the correction performance, convergence speed, and local convergence behavior of the AVOA control algorithm. For comparison, the correction performance of the adaptive optics system based on particle swarm optimization (PSO) and genetic algorithm (GA) is provided, respectively. The results show that AVOA can significantly improve the convergence speed of the adaptive optics system while ensuring global convergence. These results provide a new method for the control of wavefront sensorless AO systems, which can be applied in fields such as free-space optical communication, biological microscopy imaging, retinal imaging in the human eye, and environmental monitoring.