Design-driven aperture layout co-optimization for optical sparse aperture imaging systems

As a promising solution to the manufacturing challenges of modern super-large telescopes, the imaging performance of optical sparse aperture imaging systems (SAIS) is highly dependent on the aperture layout. This paper introduces a design-driven optimization method for SAIS that utilizes a self-adaptive genetic algorithm to concurrently optimize the number, dimensions, and spatial distribution of sub-apertures under given constraints. Departing from conventional approaches that employ fixed sub-aperture counts, this technique achieves comprehensive parameter co-optimization. Applied to a 2 m aperture system with fill factors of 15%, 20%, and 25%, the proposed method demonstrates superior performance over both classical configurations and prior optimized arrays. Our methodology provides a novel tool, to our knowledge, to fundamentally bridge theoretical design and practical implementation challenges in SAIS development.