A Pittsburgh Multi-Objective Classifier for user preferred trajectories and flight navigation

An efficient design of a Multi-Objective Learning Classifier System for multi-flight navigation is presented. A classifier is represented by a set of rules, which are used to simultaneously navigate all the flights in the airspace. Navigation of a flight is based on the relation of the flight with factors of the air traffic environment such as wind, storm as well as other flights. This system continually learns and refines the rules of classifiers by a multi-objective optimization algorithm - NSGAII - to discover the trade-off set of classifiers which navigate flights without any conflict, minimal distance of flying, minimal discomfort defined by storm level and the time duration of flights passing through storm areas, and minimizing total delay time of flights. We propose to detect conflicts between flights by grouping trajectory segments in 3-D (abscissa-x, ordinate-y, and time-t) boxes. The conflict detection is only implemented in a box, thus the number of conflict detection times approximates to the number of conflicts. Further, conflicts between flights are resolved using a hill climber by propagating delays in the takeoff time of conflicting flights. The advantage of the proposed system is that the classifier outputs its rules in a symbolic representation, making the overall process transparent to the user and reusable. Moreover, the system successfully discovered rules in all runs to optimize its performance.