<p>To cope with the increasing number of flights, optimizing arrival flights sequencing is the key to alleviating airport congestion and improving operational efficiency. It is essentially a large-scale planning problem and also belongs to NP-Hard problems. This paper proposes a Modified Whale Optimization Algorithm (MWOA) to solve this problem and the improvement consists of four major mechanisms. The first mechanism is a nonlinear dynamic strategy based on Branin function for updating the control parameter to balance the exploration and exploitation abilities. The second mechanism is the chaotic mutation based on a sine function to avoid falling into local optimum. The third mechanism is the mirror selection strategy adopted in iteration to increase convergence speed. The fourth mechanism is the early maturity detection through Gauss vibration to improve local mining and global searching abilities. The ability of the proposed algorithm to solve 25 well-known benchmark test functions is evaluated and compared to that of a series of the latest and popular metaheuristic methods reviewed in the research literature. Then all the mentioned algorithms are applied to solve the dynamic arrival flights sequencing model. The simulation results show MWOA can improve the landing efficiency by 16.90% compared with the current practical operation (first-come-first- serve), and also has different degrees of improvement compared with other comparative algorithms. In addition, the comparisons of population diversity, convergence speed, the ability to jump out of local optimum, solving time and the results of the Wilcoxon test all prove the superior performance of MWOA in solving this complicated optimization problem.</p>