Hybridized encoding for evolutionary multi-objective optimization of air traffic network flow: A case study on China. (July 2018)

• Record Type: Journal Article
• Authors: Xiao, Mingming; Cai, Kaiquan; Abbass, Hussein A.
• Is Part Of: Transportation research. Volume 115(2018)
• Page Start: 35
• Page End: 55
• Publication Date: 2018-07
• Subjects: Air traffic management -- Flow optimization -- Memetic computing -- Interdependent optimization; Logistics -- Periodicals; Transportation -- Periodicals; 388.011
• DOI: 10.1016/j.tre.2018.04.011
• Languages: English
• ISSNs: 1366-5545

Abstract

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.