Hybrid Particle Swarm Optimization with Parameter Fixing: Application to Automatic Taxi Management

摘要

The conflicting needs of meeting the growth of air traffic, mitigating the resulting additional air pollution, andreducing costs derived from congestion at airports will push toward the modernization of ground operations andairport management. In this framework, a new solution is proposed to perform just-in-time taxi operations usingautonomous electric towbarless tractors. The purpose of this solution was to eliminate queues and to reduce theenvironmental and economic impacts of ground operations so as to meet the requirements for future air trafficmanagement. A hybrid particle swarm optimization algorithm is developed to provide conflict-free schedules fortractor autopilots. To improve the rate of convergence of the algorithm, a parameter-fixing algorithm has beendeveloped, which constrains the particle elements based on the particle history.