Air traffic modeling and optimization by solving two new models with a modified algorithm

2023;
: pp. 712–719
https://doi.org/10.23939/mmc2023.03.712
Received: February 18, 2023
Accepted: July 09, 2023

Mathematical Modeling and Computing, Vol. 10, No. 3, pp. 712–719 (2023)

1
Department of Mathematics and Computing, University Hassan II, LMFA, Casablanca, Morocco
2
Department of Mathematics and Computing, University Hassan II, LIMSAD, Casablanca, Morocco
3
Department of Mathematics and Computing, University Hassan II, LMFA, Casablanca, Morocco
4
Laboratory Signals, distributed systems and artificial intelligence, Higher normal school of technical education ENSET, University Hassan II, Mohammedia, Morocco

In this paper, we will try to manage arrivals in the approach area of Mohammed V airport by solving two proposed algorithms "Scheduling with speeds limitations" and "Scheduling with speeds corrections", with a modified Bat algorithm, this modification involved the speed equation as well as the frequency equation of the standard Bat algorithm to avoid the phenomenon of slow convergence to the best solution in the search space of this algorithm.  These proposed algorithms will allow us to schedule traffic optimally and to increase the capacity of the airport and better manage the controlled space approach area of Mohammed V Airport.

  1. Hu X.-B., Di Paolo E.  An efficient genetic algorithm with uniform crossover for air traffic control.  Computers & Operations Research.  36 (1), 245–259 (2009).
  2. Alonso-Ayuso A., Escudero L. F., Martín-Campo F. J., Mladenović N.  VNS based algorithm for solving a $0-1$ nonlinear nonconvex model for the Collision Avoidance in Air Traffic Management.  Electronic Notes in Discrete Mathematics.  39, 115–120 (2012).
  3. Almufti S.  Using Swarm Intelligence for solving NP-Hard Problems.  Academic Journal of Nawroz University.  6 (3),46–50 (2017).
  4. Yang X.-S.  A New Metaheuristic Bat-Inspired Algorithm.  Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). 65–74 (2010).
  5. Zebari A. Y., Almufti S. M., Abdulrahman C. M.  Bat algorithm (BA): review, applications and modifications.  International Journal of Scientific World.  8 (1), 1–7 (2020).
  6. Idrissi O., Bikir A., Qbadou M., Mansouri K., Youssfi M.  Enhancing Air traffic flow arrival management by optimizing speed profile: Casablanca Terminal Area Case study.  2020 IEEE International conference of Moroccan Geomatics (Morgeo). 1–5 (2020).
  7. Wu S., Liu X.  Optimized Sequencing and Scheduling Algorithms for Arrival Air Traffics Based on FCFS Principles.  IFAC Proceedings.  27 (12), 215–218 (1994).
  8. Khadija E. H., Samira E., Othmane I., Samira B.  A new modified Bat Algorithm for Air Traffic Management.  2021 Third International Conference on Transportation and Smart Technologies (TST). 85–89 (2021).
  9. Clerc M., Siarry P.  Une nouvelle métaheuristique pour l'optimisation difficile : la méthode des essaims particulaires.  J3eA, Journal sur l'enseignement des sciences et technologies de l'information et des systèmes.  3, 007 (2004).
  10. Tchomté S. K., Gourgand M.  Particle swarm optimization: A study of particle displacement for solving continuous and combinatorial optimization problems.  International Journal of Production Economics.  121 (1), 57–67 (2009).