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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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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