The migration operation is a crucial part of Biogeography-based optimization (BBO) algorithm, and improving the migration rate model can enhance information exchange and the global search capability of the algorithm. To address the weak search capability issue of the traditional BBO using a linear migration rate model, a circular arc migration rate model is proposed, where the migration rate function curve gradually decreases (or increases) with the number of species and then rapidly decreases (or increases). This model performs better in achieving global optimization. Firstly, the principle and process of BBO are introduced, and the migration rate model, which is the core of the algorithm, is established. Finally, the BBO with the proposed circular arc migration rate model is compared and analyzed against the BBO with linear, cosine, and hyperbolic tangent migration rate models. Through performance testing and comparative analysis on 12 typical test functions, the results show that compared to the linear, cosine, and hyperbolic tangent migration rate models, the BBO with the circular arc migration rate model demonstrates improvements in terms of optimization minimum value, average minimum value, and variance performance indicators.