Comprehensive learning multi-objective particle swarm optimizer for crossing waypoints location in air route network

The optimization of national Air route network (ARN) has become an effective method to improve the safety and efficiency of air transportation. The Crossing waypoints location (CWL) problem is a crucial problem in the design of ARN. This paper formulates a multi-objective model for the CWL problem, and presents a Comprehensive learning multi-objective particle swarm optimizer (CLMOPSO) to minimize both airlines cost and flight conflicts. The application to redesign national ARN of China shows the proposed optimizer valid and effective by comparison with the conventional optimization algorithms. The application of the proposed methodology can also serve as a benchmark application as shown in the paper.