A fitness sharing based ant clustering method for multimodal optimization of the aircraft longitudinal automatic carrier landing system

Abstract

With the flight envelope becoming larger and larger, the Automatic Carrier Landing System (ACLS) is becoming a complex large-scale system, and the corresponding control parameter design has been the most important key link to ensure the safety and flight quality of aircraft carrier landing missions. In this paper, a Fitness Sharing based Ant Clustering (FSAC) method is presented for use in multimodal optimization of the longitudinal ACLS and to reveal the hidden properties of the solution space. In doing so, first, the lattice rule based space sampling strategy is used to create the individual ant sequences. Then a fading memory fitness sharing function is applied to modify the clustering strategy and regroup the ants for multimodal optimization of the search space. An adaptive learning strategy is developed to dynamically adjust the search scope of the ant colony. Moreover, an online health monitor is used to replace the weak ants with new ones in a timely manner in order to keep robustness of the FSAC. Finally, the multimodal feasible solutions which are good candidates for the ACLS design are presented and the characteristics of the solution space are also analyzed. The result shows that many large solution sets are located at the bottom of the solution space, whereas on the upper side of the solution space, the number of feasible solutions decreases sharply. An F/A-18 model is used as a test bed and the simulations are carried out in various wind conditions to demonstrate the effectiveness and feasibility of the proposed method.