The mining missing links and predicting upcoming links are two important topics in the link prediction. In the past decades, a variety of algorithms have been developed, the majority of which apply similarity measures to estimate the bonding probability between nodes. And for these algorithms, it is still difficult to achieve a satisfactory tradeoff among precision, computational complexity, robustness to network types, and scalability to network size. In this article, we propose a chaotic ant colony optimized (CACO) link prediction algorithm, which integrates the chaotic perturbation model and ant colony optimization. The extensive experiments on a wide variety of unweighted and weighted networks show that the proposed algorithm CACO achieves significantly higher prediction accuracy and robustness than most of the state-of-the-art algorithms. The results demonstrate that the chaotic ant colony effectively takes advantage of the fact that most real networks possess the transmission capacity and provides a new perspective for future link prediction research.