Performance comparison of population-based optimization algorithms for air traffic control

Abstract

<jats:title content-type="abstract-subheading">Purpose</jats:title> <jats:p>The purpose of this paper is to present a detailed performance comparison of recent and state-of-the-art population-based optimization algorithms for the air traffic control problem.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title> <jats:p>Landing sequence and corresponding landing times for the aircrafts were determined by using population-based optimization algorithms such as artificial bee colony, particle swarm, differential evolution, biogeography-based optimization, simulated annealing, firefly and teaching–learning-based optimization. To obtain a fair comparison, all simulations were repeated 30 times for each of the seven algorithms, two different problems and two different population sizes, and many different criteria were used.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Findings</jats:title> <jats:p>Compared to conventional methods that depend on a single solution at the same time, population-based algorithms have simultaneously produced many alternate possible solutions that can be used recursively to achieve better results.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Research limitations/implications</jats:title> <jats:p>In some cases, it may take slightly longer to obtain the optimum landing sequence and times compared to the methods that give a direct result; however, the processing times can be reduced using powerful computers or GPU computations.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Practical implications</jats:title> <jats:p>The simulation results showed that using population-based optimization algorithms were useful to obtain optimal landing sequence and corresponding landing times. Thus, the proposed air traffic control method can also be used effectively in real airport applications.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Social implications</jats:title> <jats:p>By using population-based algorithms, air traffic control can be performed more effectively. In this way, there will be more efficient planning of passengers’ travel schedules and efficient airport operations.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Originality/value</jats:title> <jats:p>The study compares the performances of recent and state-of-the-art optimization algorithms in terms of effective air traffic control and provides a useful approach.</jats:p> </jats:sec

Original document

The different versions of the original document can be found in:

• https://avesis.erciyes.edu.tr/publication/details/ddbb813a-8828-42f0-94c1-40ac55127d9b/oai

• https://www.emerald.com/insight/content/doi/10.1108/AEAT-10-2019-0212/full/html,

https://academic.microsoft.com/#/detail/3019686124

• https://www.emerald.com/insight/content/doi/10.1108/AEAT-10-2019-0212/full/xml,

https://www.emerald.com/insight/content/doi/10.1108/AEAT-10-2019-0212/full/html,

http://dx.doi.org/10.1108/aeat-10-2019-0212 under the license https://www.emerald.com/insight/site-policies