Simulation-Based Evolutionary Optimization of Air Traffic Management

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 Manage...

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Main Authors: Alessandro Pellegrini, Pierangelo Di Sanzo, Beatrice Bevilacqua, Gabriella Duca, Domenico Pascarella, Roberto Palumbo, Juan Jose Ramos, Miquel Angel Piera, Gabriella Gigante
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9184863/
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spelling doaj-3f5acabe40484ca7bce5e3274bd37b0c2021-03-30T03:31:54ZengIEEEIEEE Access2169-35362020-01-01816155116157010.1109/ACCESS.2020.30211929184863Simulation-Based Evolutionary Optimization of Air Traffic ManagementAlessandro Pellegrini0https://orcid.org/0000-0002-0179-9868Pierangelo Di Sanzo1https://orcid.org/0000-0001-6136-6303Beatrice Bevilacqua2Gabriella Duca3Domenico Pascarella4https://orcid.org/0000-0003-1332-4234Roberto Palumbo5https://orcid.org/0000-0003-1569-1812Juan Jose Ramos6Miquel Angel Piera7https://orcid.org/0000-0002-7227-7944Gabriella Gigante8Lockless S.r.l., Rome, ItalyLockless S.r.l., Rome, ItalyInstititute for Sustainable Society and Innovation (ISSNOVA), Naples, ItalyInstititute for Sustainable Society and Innovation (ISSNOVA), Naples, ItalyCentro Italiano Ricerche Aerospaziali (CIRA), Capua, ItalyCentro Italiano Ricerche Aerospaziali (CIRA), Capua, ItalyDepartment of Telecommunication and Systems Engineering, Universitat Autònoma de Barcelona (UAB), Barcelona, SpainDepartment of Telecommunication and Systems Engineering, Universitat Autònoma de Barcelona (UAB), Barcelona, SpainCentro Italiano Ricerche Aerospaziali (CIRA), Capua, ItalyIn 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.https://ieeexplore.ieee.org/document/9184863/Air traffic controldistributed optimizationevolutionary algorithmsmodeling and simulationmulti-objective optimizationsupport to strategic design
collection DOAJ
language English
format Article
sources DOAJ
author Alessandro Pellegrini
Pierangelo Di Sanzo
Beatrice Bevilacqua
Gabriella Duca
Domenico Pascarella
Roberto Palumbo
Juan Jose Ramos
Miquel Angel Piera
Gabriella Gigante
spellingShingle Alessandro Pellegrini
Pierangelo Di Sanzo
Beatrice Bevilacqua
Gabriella Duca
Domenico Pascarella
Roberto Palumbo
Juan Jose Ramos
Miquel Angel Piera
Gabriella Gigante
Simulation-Based Evolutionary Optimization of Air Traffic Management
IEEE Access
Air traffic control
distributed optimization
evolutionary algorithms
modeling and simulation
multi-objective optimization
support to strategic design
author_facet Alessandro Pellegrini
Pierangelo Di Sanzo
Beatrice Bevilacqua
Gabriella Duca
Domenico Pascarella
Roberto Palumbo
Juan Jose Ramos
Miquel Angel Piera
Gabriella Gigante
author_sort Alessandro Pellegrini
title Simulation-Based Evolutionary Optimization of Air Traffic Management
title_short Simulation-Based Evolutionary Optimization of Air Traffic Management
title_full Simulation-Based Evolutionary Optimization of Air Traffic Management
title_fullStr Simulation-Based Evolutionary Optimization of Air Traffic Management
title_full_unstemmed Simulation-Based Evolutionary Optimization of Air Traffic Management
title_sort simulation-based evolutionary optimization of air traffic management
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description 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.
topic Air traffic control
distributed optimization
evolutionary algorithms
modeling and simulation
multi-objective optimization
support to strategic design
url https://ieeexplore.ieee.org/document/9184863/
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