文献类型：期刊文献

英文题名：Hybridized encoding for evolutionary multi-objective optimization of air traffic network flow: a case study on China

作者：Mingming Xiao[1]; Kaiquan Cai[2]; Abbass, H.A.[3]

机构：[1] Smart City Coll., Beijing Union Univ., Beijing, China; [2] Sch. of Electron. & Inf. Eng., Beihang Univ., Beijing, China; [3] Sch. of Eng. & Inf. Technol., Univ. of New South Wales, Sydney, NSW, Australia

第一机构：北京联合大学继续教育学院

年份：0

卷号：115

起止页码：35-55

外文期刊名：Transportation Research Part E: Logistics and Transportation Review

收录：EI(收录号：18116536)

语种：英文

外文关键词：air traffic control - evolutionary computation - genetic algorithms

摘要：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. [All rights reserved Elsevier].