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

Mingming Xiao, Kaiquan Cai and Hussein A. Abbass

Transportation Research Part E: Logistics and Transportation Review, 2018, vol. 115, issue C, 35-55

Abstract: 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.

Keywords: Air traffic management; Flow optimization; Memetic computing; Interdependent optimization (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.tre.2018.04.011

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