Combining large-scale biological data, using computational methods to mine potential disease-gene associations is a popular strategy. At the same time, bio-inspired intelligent optimization has always been a hot research field of intelligent computing. In this study, we apply the pigeon-inspired optimization (PIO) algorithm to the identification of human disease-genes. The problem of predicting disease-genes is translated into a single-objective optimization problem. A reasonable objective function is designed to measure the association between genes and inquiring diseases in a heterogeneous network, and the corresponding probability matrix is generated. The experimental results show that the proposed method (PDG-PIO) can accurately identify disease-genes.