A Self-Adaptive Hybrid Algorithm for Planning City Air Terminals

City air terminals are small-scale terminals located in city center for airport transport service. To optimize the locations of city air terminals, first, a mathematic model of the problem is introduced. Three aspects including the average distance from passengers to city air terminals, the maximum tolerable path length, and the maximum terminal volume are considered. Then, a novel hybrid algorithm is proposed. In the method, the ripple-spreading algorithm is applied for solving the many-to-many path optimization problem and a genetic algorithm is used for locating city air terminals. In order to improve the performance and increase the convergence speed, a self-adaptive genetic algorithm is further developed, varying the genetic operations and the number of generations according to the current convergence status. A test case is set up based on the city center of Tianjin, China. The proposed method is tested and compared to some other existing methods to show its effectiveness and efficiency.