Improved heuristic and evolutionary methods for tactical missile mission planning

In this paper, improved heuristic and evolutionary methods are presented for pre-launch trajectory optimization of a tactical missile. Computation time and trajectory length are minimized as objectives whereas maneuverability of the missile, total amount of fuel, obstacles, no-fly zones, islands, shorelines are considered to be constraints. There are two main methods developed for this purpose. One method is based on heuristic search by adaptive-based A* algorithm. In this method, sub-optimal path is obtained by constructing a network in which node distance (leg length) and node intensity can be changed adaptively with respect to mission environment in the search time. This enhancement in conventional A* method leads to closer optimal trajectories in less computation load especially in complex mission scenarios such as shorelines having narrow pass, too many unintended targets or friends etc. The other proposed path planning method is improved genetic algorithm. This algorithm has a variable-length chromosome and a real-valued encoding as well as an intelligent population creation method that produces feasible individuals only. By means of starting with a feasible population, it is observed that convergence time is far less than using random creation of initial population. Furthermore, for rapid analysis and comparison of the two proposed methods in different environments, a generic graphical user interface (MPT-The Mission Planning Tool) is developed.