A Multi-objective Memetic Algorithm for Network-Wide Flights Planning Optimization

The ever-growing air traffic flow has brought about great challenges to balance the airspace congestion and air traffic demands. This fact sparks numerous studies on Network-wide Flights Planning Optimization (NFPO) which aims to reconcile the contradiction between flight delay cost and airspace con...

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Vydáno v:2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI) s. 518 - 525
Hlavní autoři: Yan, Su, Cai, Kaiquan, Zhu, Yanbo
Médium: Konferenční příspěvek Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.11.2015
Témata:
Air traffic
Airspace
Algorithms
Artificial intelligence
Correlation
Delays
Flight planning
Memetics
Multi-objective Memetic Algorithm
Network-wide Flights Planning Optimization
Optimization
Planning
Rerouting Meme
Search problems
Searching
Trajectories
Trajectories Correlation
Trajectory
ISSN:1082-3409
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Abstract The ever-growing air traffic flow has brought about great challenges to balance the airspace congestion and air traffic demands. This fact sparks numerous studies on Network-wide Flights Planning Optimization (NFPO) which aims to reconcile the contradiction between flight delay cost and airspace congestion by optimizing the pre-strategic flight plans from a network-wide point of view. In consideration of bi-objective and large-scale characteristics of the NFPO problem, this paper proposes a multi-objective Memetic Algorithm with Rerouting Meme (MARM) that incorporates an evolutionary global search framework with a problem-specific meme ulocal search operator. With the idea of heuristically reducing the interactions among flight trajectories to decongest the airspace, the Trajectories Correlation (TC) is defined as key network-wide knowledge and is applied to design the critical Rerouting Meme (RM). Additionally, to balance the ability of exploitation and exploration, the idea of simulated heating configuration setting is adopt for RM to integrate with the global search. Extensive empirical studies conducted on real large-scale traffic data of China air traffic network and flight plans support that MARM is beneficial to the NFPO problem via showing the improvement on effectiveness.
AbstractList The ever-growing air traffic flow has brought about great challenges to balance the airspace congestion and air traffic demands. This fact sparks numerous studies on Network-wide Flights Planning Optimization (NFPO) which aims to reconcile the contradiction between flight delay cost and airspace congestion by optimizing the pre-strategic flight plans from a network-wide point of view. In consideration of bi-objective and large-scale characteristics of the NFPO problem, this paper proposes a multi-objective Memetic Algorithm with Rerouting Meme (MARM) that incorporates an evolutionary global search framework with a problem-specific meme ulocal search operator). With the idea of heuristically reducing the interactions among flight trajectories to decongest the airspace, the Trajectories Correlation (TC) is defined as key network-wide knowledge and is applied to design the critical Rerouting Meme (RM). Additionally, to balance the ability of exploitation and exploration, the idea of simulated heating configuration setting is adopt for RM to integrate with the global search. Extensive empirical studies conducted on real large-scale traffic data of China air traffic network and flight plans support that MARM is beneficial to the NFPO problem via showing the improvement on effectiveness.
Author Kaiquan Cai
Su Yan
Yanbo Zhu
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Snippet The ever-growing air traffic flow has brought about great challenges to balance the airspace congestion and air traffic demands. This fact sparks numerous...
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StartPage 518
SubjectTerms Air traffic
Airspace
Algorithms
Artificial intelligence
Correlation
Delays
Flight planning
Memetics
Multi-objective Memetic Algorithm
Network-wide Flights Planning Optimization
Optimization
Planning
Rerouting Meme
Search problems
Searching
Trajectories
Trajectories Correlation
Trajectory
Title A Multi-objective Memetic Algorithm for Network-Wide Flights Planning Optimization
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