Organ transplantation faces significant challenges due to the limited availability of donor organs, necessitating the exploration of innovative alternatives. Bioprinting, a dynamic form of 3D printing, offers a promising solution by creating complex biological structures layer by layer. This study focuses on addressing the dynamic optimization challenges in in-situ bioprinting through the integration of Ant Colony Optimization (ACO) within the Computational Intelligence Aided Design (CIAD) framework. The dynamic nature of bioprinting environments, characterized by shifting obstacles and changing targets, requires robust algorithms that can adapt to these variations in real-time. ACO, inspired by the foraging behavior of ant colonies, provides effective global optimization with low complexity, making it well-suited for these dynamic conditions. The methodology includes environmental modeling, adaptive path selection, and dynamic dead zone escape through improved state transition rules. Simulations conducted using MATLAB demonstrate that ACO ensures complete area coverage, minimizes track repetition, and significantly reduces the number of turns, thus enhancing the efficiency and success rate of bioprinting. These findings highlight the potential of ACO in advancing dynamic optimization techniques for bioprinting, contributing to the broader fields of evolutionary dynamic optimization and regenerative medicine.