Study On Aircraft Route Planning Based On Improved Ant Colony Algorithm

With the rapid development to computer, automation and information technology, modern air technology changes for the better day by day. The aircraft has more and more types and its control has become increasingly complex. Whether in war or in civilian areas, with the increasing of the difficulty, criticality and intensity of modern flying missions, the pilot's physical and psychological stress will increase. By usingRoute Planning technology, we can improve mission success rate and aircraft operational efficiency, and reduce cost. It guarantees safe and reliable flight. Route planning technology is one of the core technologies of Mission Planning System. It means to plan a best or satisfied route for aircraft under the comprehensive consideration of all kinds of factors, such as arrival time, fuel consumption, threats in flight environment and so on, so that the aircraft can complete the mission and return to base safely. However, route planning becomes a very challenging problem in mission plan, for the flight environment is complex and the constraints which should be considered has too many types and strong coupling. For this problem, researchers have made a number of route planning algorithms, such as Genetic Algorithm, A-Star algorithm, the Artificial Potential Field, Voronoi Algorithm, et al. But these planning methods will appear this or that shortcoming in practical application.In this paper, ant colony algorithm is discussed, introducing its basic principles and mathematical model and summarizing its advantages and disadvantages .And then the main reasons why the algorithm stagnation occurs or algorithm converges slow are analyzed. After that, A typical improved ant colony algorithm(MAX-MIN Ant System) is introduced, on the basis of which a new strategy of ant colony algorithm to improve is proposed------it is called"dynamic multi-colonies ant colony algorithm". Its basic idea is: groups of ants which are from different colonies search paths independently, instead of only one colony searching in basic ant colony algorithm, but they can exchange pheromone on corresponding path when the algorithm stagnation occurs, and at the same time the volatile coefficient of the pheromone is adjusted bi-directionally. By the pheromones of every colony shaking repeatedly, the searching space of the algorithm is expanded. At last, the basic ant colony algorithm and its improved algorithm is used to solve a Traveling Salesman Problem. By comparing the MATLAB simulation results, it is proved that the algorithm is effective. In order to further verify the usefulness of the improved ant colony algorithm, and to prove that it is feasibility and effective that the improved algorithm is used in route planning, a planning task instance is set. Using the basic algorithm and its improved algorithm the task is solved by MATLAB simulation. From the comparison of the results of the two algorithms, we can see that, the improved ant colony algorithm is better that the basic both in search capability and stability.