文献类型：期刊文献

英文题名：Optimizing the safety-efficiency trade-off on nationwide air traffic network flow using cooperative co-evolutionary paradigm

作者：Xiao, Mingming[1];Hong, Chen[2,3];Cai, Kaiquan[4]

通讯作者：Xiao, MM[1]

机构：[1]Beijing Union Univ, Coll Smart City, Beijing 100101, Peoples R China;[2]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[3]Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China;[4]Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China

第一机构：北京联合大学

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Smart City, Beijing 100101, Peoples R China.|[11417]北京联合大学;

年份：2025

卷号：15

期号：1

外文期刊名：SCIENTIFIC REPORTS

收录：;Scopus(收录号：2-s2.0-105009700501);WOS:【SCI-EXPANDED(收录号:WOS:001522992900019)】；

基金：This work was supported by the State Key Laboratory of Air Traffic Management System (No. SKLATM202201), the Academic Research Projects of Beijing Union University (No. ZK30202304), and the National Key R&D Program of China (No.2018AAA0100804).

语种：英文

外文关键词：Air traffic network flow optimization; Large-scale; Multi-objective optimization; Cooperative co-evolution

摘要：Safety and efficiency are two classical conflicting objectives in the air traffic system: an increase in efficiency may come at the cost of increasing density of aircraft in the space, which increases collision risk and controllers' workload. Nationwide air traffic network flow optimization (ATNFO) is an effective way to pursue trade-offs between safety and efficiency by optimizing flight departure time-slots and routes within a given time period and under the latest airspace resources. Solving a national ATNFO problem is usually bedeviled by "the curse of dimensionality" as it consists of a huge number of variables. This paper presents a specific "divide-and-conquer" based approach, namely H-COEA, to solve it. Firstly, an effective chromosome representative scheme, which can be naturally divided into 3 sub-components, i.e., the departure time-slots, the heuristic for selecting flight route, and the timetabling indicating the order and fairness for flight to select route, is employed. And then, the corresponding 3 sub-populations are co-evolved based on a Cooperative Co-evolution (CC) paradigm. Four different-scale ATNFO problems are solved with H-COEA and the state-of-the-art multi-objective evolutionary algorithms. Results show that H-COEA obtains better trade-offs between safety and efficiency, making CC paradigm appropriating for solving large-scale ATNFO problem.