Flight Schedule Problem Optimization Based on Discrete Memory-Enhanced Restructured Particle Swarm Optimization Algorithm

Flight Schedule Problem optimization is a typical NP-hard combinatorial optimization problem that is challenging to solve using traditional algorithms, so metaheuristic algorithms are commonly adopted for such problems. This paper proposes a Discrete Memory-Enhanced Restructured Particle Swarm Optimization algorithm (DMERPSO) to address Flight Scheduling Problem optimization. Firstly, this paper designs a hybrid particle encoding scheme capable of simultaneously handling flight time adjustments (integer variables) and route selections (categorical variables) for the Flight Schedule Problem. Secondly, a new update equation of particle positions is provided based on probability selection within the three terms of the Memory-Enhanced Restructured Particle Swarm Optimization (MERPSO) algorithm, and the calculation of the selection probability is designed. Thirdly, the two strategies and perturbation terms of MERPSO are improved in order to be adapted to optimize the discrete Flight Schedule Problem. Finally, simulation experiments are conducted using DMERPSO based on real flight data from multiple Chinese airports with the objective of minimizing total flight delays, leading to better solutions that are faster than various benchmark algorithms. The DMERPSO algorithm exhibits significant advantages in reducing total delays, improving solution stability, and enhancing robustness, which validates that DMERPSO provides an effective new approach for solving Flight Schedule Problem optimization problems.