Research On Intelligence Optimization Method For Aircraft Landing Scheduling

As one of the core contents of Air Traffic Flow Management (ATFM) in terminal area, Aircraft Landing Scheduling (ALS) determines an efficient landing scheduling scheme for a given set of aircrafts in order to insure the safety of aircrafts. Researches about ALS problem have great significance for flight safety and improving flight benefit. First-Come-First-Served can schedule the landing aircrafts very simply and rapidly, however it may fail to provide a reasonable scheduling scheme under dense aircrafts. Until now, there are two kinds of scheduling algorithms for addressing ALS problem, one is Linear Programming (LP) algorithm, and the other is Computational Intelligence (CI) algorithm. LP shows high effectiveness and correctness, however it can hardly find the global optimal solution owing to lack of the ability for global search. Nevertheless CI not only has the powerful ability for global search, but also is able to handle nonlinear constraint and goal function. Therefore it has become a hot topic to resolve ALS using CI. However CI likely produces large computational cost especially at busy airports. Thereby CI needs to combine some heuristic methods to address ALS better.In this paper, we establish an ALS model and proposed a Hybrid Algorithm of Clonal Selection Algorithm and Receding Horizon Control (CSA-RHC) based on this model.Based on the multi-constraint characteristic of ALS problem, a effective constraint handling strategy is proposed, which involves the encoding strategy with constraint handling and immune operators based on infeasibility degree. The new encoding strategy reduces the number of constraint effectively because the constraint magnitude is decreased from O( n 2)to O ( n ). We redesign the clone, clonal mutation, and clonal selection operators based on infeasibility degree. The proposed constraint handling strategy obtained better solution than other methods. The constraint handling ability of constraint handling strategy is extensively verified.In order to speed up search, Excellent Gene Segment Spread (EGSS) is designed. During each receding horizon, some gene segments after the scheduling using CSA obtain very useful information, which can be made full use of during the next receding horizon. The excellent gene segments can be spread over the entire receding horizon to improve the speed of searching the optimal solution.