Hybridized encoding for evolutionary multi-objective optimization of air traffic network flow: A case study on China

This paper presents a novel hybridized indirect and direct encoding (HybrID) genetic algorithm for solving air traffic network flow optimization problems. A heuristic, which uses the Dijkstra algorithm for generating different types of shortest paths on a graph while controlling the weights on each...

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Bibliographic Details
Published in:Transportation research. Part E, Logistics and transportation review Logistics and transportation review, 2018-07, Vol.115, p.35-55
Main Authors: Xiao, Mingming, Cai, Kaiquan, Abbass, Hussein A.
Format: Article
Language:English
Subjects:
Air traffic management
Flow optimization
Interdependent optimization
Memetic computing
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Summary:This paper presents a novel hybridized indirect and direct encoding (HybrID) genetic algorithm for solving air traffic network flow optimization problems. A heuristic, which uses the Dijkstra algorithm for generating different types of shortest paths on a graph while controlling the weights on each arc, is proposed for selecting optimal flight routes based on current air traffic. A novel HybrID chromosome representation is employed along with the proposed heuristic and a genetic algorithm for optimization. Experiments on synthetic problems and real data of the Chinese airspace show the proposed method outperforms the direct encoding method on efficiency and efficacy metrics.
ISSN:1366-5545
1878-5794
DOI:10.1016/j.tre.2018.04.011