With the surge in passenger consumption after the epidemic, flights have increased significantly, and the operational efficiency of hub airports has been challenged. Traditional flight scheduling methods are mainly based on static planning and manual empirical decision making, which are difficult to adapt to the dynamic, variable and complex operational environment. Therefore, this study proposes to introduce the Imperial Competition Algorithm (ICA) into the study of the flight sequencing problem, with a view to improving the efficiency and effectiveness of flight sequencing through intelligent optimization algorithms. The study takes flight sequencing at Atlanta International Airport as the research object, constructs a flight scheduling model considering the airport runway configuration and operation mode, and designs and implements a simulation process with ICA. The experimental results show that compared with the first-come-first-served (FCFS) model, the proposed model can significantly reduce the flight delays and improve the runway throughput to a certain extent, which verifies the effectiveness and superiority of the model. The study provides new theoretical and practical support for the inbound sequencing flight problem and promotes the application of intelligent optimization algorithms in air traffic management.