Simulation-Based Evolutionary Optimization of Air Traffic Management
Pellegrini, Alessandro (Instititute for Sustainable Society and Innovation (Italy))
Sanzo, Pierangelo Di (Instititute for Sustainable Society and Innovation (Italy))
Bevilacqua, Beatrice (Instititute for Sustainable Society and Innovation (Italy))
Duca, Gabriella (Instititute for Sustainable Society and Innovation (Italy))
Pascarella, Domenico (Centro Italiano Ricerche Aerospaziali (Italy))
Palumbo, Roberto (Centro Italiano Ricerche Aerospaziali (Italy))
Ramos González, Juan José (Universitat Autònoma de Barcelona. Departament de Telecomunicació i Enginyeria de Sistemes)
Piera, Miquel Àngel (Universitat Autònoma de Barcelona. Departament de Telecomunicació i Enginyeria de Sistemes)
Gigante, Gabriella (Centro Italiano Ricerche Aerospaziali (Italy))

Date:	2020
Description:	20 pàg.
Abstract:	In the context of aerospace engineering, the optimization of processes may often require to solve multi-objective optimization problems, including mixed variables, multi-modal and non-differentiable quantities, possibly involving highly-expensive objective function evaluations. In Air Traffic Management (ATM), the optimization of procedures and protocols becomes even more complicated, due to the involvement of human controllers, which act as final decision points in the control chain. In this article, we propose the use of computational intelligence techniques, such as Agent-Based Modelling and Simulation (ABMS) and Evolutionary Computing (EC), to design a simulation-based distributed architecture to optimize control plans and procedures in the context of ATM. We rely on Agent-Based fast-time simulations to carry out offline what-if analysis of multiple scenarios, also taking into account human-related decisions, during the strategic or pre-tactical phases. The scenarios are constructed using real-world traffic data traces, while multiple optimization variables governed by an EC algorithm allow to explore the search space to identify the best solutions. Our optimization approach relies on ad-hoc multi-objective performance metrics which allow to assess the goodness of the control of aircraft and air traffic regulations. We present experimental results which prove the viability of our approach, comparing them with real-world data traces, and proving their meaningfulness from an Air Traffic Control perspective.
Grants:	European Commission 783189
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Air traffic control ; Distributed optimization ; Evolutionary algorithms ; Modeling and simulation ; Multi-objective optimization ; Support to strategic design
Published in:	IEEE Access, Vol. 8 (September 2020) , p. 9184863-161570, ISSN 2169-3536

DOI: 10.1109/ACCESS.2020.3021192

20 p, 3.0 MB

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