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

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  • Xiao, Mingming
  • Cai, Kaiquan
  • Abbass, Hussein A.

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.

Suggested Citation

  • Xiao, Mingming & Cai, Kaiquan & Abbass, Hussein A., 2018. "Hybridized encoding for evolutionary multi-objective optimization of air traffic network flow: A case study on China," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 115(C), pages 35-55.
    • Handle: RePEc:eee:transe:v:115:y:2018:i:c:p:35-55
    DOI: 10.1016/j.tre.2018.04.011
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    References listed on IDEAS

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    Cited by:

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    5. Muren, & Wu, Jianjun & Zhou, Li & Du, Zhiping & Lv, Ying, 2019. "Mixed steepest descent algorithm for the traveling salesman problem and application in air logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 126(C), pages 87-102.

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