Airport Gate Allocation Using the Distributed Sub-population Enhanced Differential Evolution Algorithm

In this study, we introduce the Distributed Sub-population Enhanced Differential Evolution (DSEDE), an extension of the Success-History based Adaptive Differential Evolution (SHADE) algorithm. The DSEDE algorithm segments the population into sub-populations that evolve autonomously and share information generation ally. Applied to the aircraft gate assignment problem (GAP), involving three sub-fitness functions, a total fitness function, and six constraints, DSEDE outperforms both DE and SHADE in computational experiments, showcasing its strength in multi-objective optimization with complex constraints. This research contributes a dynamic algorithmic framework to evolutionary computation.