Aircraft landing problem using hybrid differential evolution and simple descent algorithm

The aircraft landing problem (ALP) is a practical and challenging optimization problem for the air traffic industry. ALP involves allocating a set of aircrafts to airport runways and allocating landing times for which the goal is to minimize the total cost of landing deviation from the preferred target times. Differential evolution (DE) is a population based algorithm that has been shown to be an effective algorithm for solving continuous optimization problems. However, DE can suffer from slow convergence when utilized for combinatorial optimization problems, thus hindering its ability to return good quality solutions in these domains. To address this we propose a hybrid algorithm that combines differential evolution with a simple descent algorithm. DE is responsible for exploring new regions in the search space, whilst the descent algorithm focuses the search around the area currently being explored. Experimenting with widely used ALP benchmark instances, we demonstrate that the proposed hybrid algorithm performs better than DE without the simple descent algorithm. Furthermore, performance comparisons with other algorithms from the scientific literature demonstrate that our hybrid algorithm performs better, or at least comparably, in terms of both solution quality and computational time.