An improved methodology for ARN crossing waypoints location problem

The optimization of air route network (ARN) is an effective method to improve the transmission ability of air traffic flow. One crucial issue in optimizing the national ARN is the crossing waypoints location problem (CWLP), which determines the airspace capacity, airspace safety and flight efficiency. However, the existing works always focus on one or two aspects regardless of the airspace capacity, which is the measurement of maximum transmission ability and directly determines the availability of ARN in practice. In this paper, we fully investigate the three key factors above in ARN design and formulate a triple-objectives model to solve the CWLP. Furthermore, due to challenges brought by the multi-objectives model and the large scale of CWLP, a multi-objective optimization algorithm based on comprehensive learning particle swarm optimization (CLPSO) is proposed, known as MOCLPSO. The experiments on the national ARN of China redesign are implemented which show the better performance of MOCLPSO compared with two conventional optimization algorithms that are NSGA-II and MOEA/D.