Combat Planning Of Unmanned Aerial Vehicles Based On Ant Colony Algorithm

With the development of the war situation and the improvement of the weaponry and equipment system,the coordinated of unmanned aerial vehicles(UAVs)has become the basic requirement for modern warfare and trend of development in the future.To applied the UAVs in military operations effectively,the flight paths planning and collaborative tasks assignment are two indispensable conditions.The traditional track planning and task allocation operations are too slow to meet the fighting needs and there are still many difficulties need to be overcome,so it is necessary to do research on them.The purpose of this study is to make the UAVs can complete the investigation and information collection of the suspicious target points under the limit of its own fuel oil after taking off from our base,and can get a favorable task allocation scheme which can maximizing superiority of military in the battlefield environment.Ant colony algorithm can get a satisfactory solution in a short time when solving combinatorial optimization problems,which makes it more suitable for the rapidly changing attack-defense environment.However,there still have some shortcomings,according to which some works have done in this paper as follows:(1)During the investigative track planning of military UAVs,to overcome the shortages of ant colony algorithm,adaptive adjustment was made on the evaporation coefficient to take both initial search range and convergence speed at later stage into account.Moreover,the nodes exchange local search strategy was used to avoid premature and accelerate convergence.(2)In order to improve the algorithm's probability of getting the optimal solution,we used two kinds of independent ant colonies to search the routes,each of them could use their different pheromone to help the other group find the better routes.(3)Based on the traditional ant colony algorithm,we adopt new heuristic rules to define the heuristic function,combined the value of the tactical target with the heuristic information to guide the ants to transfer to the point of high tactical value and make attack task morepertinent when solving the task allocation problem of military UAVs.Considered the actual situation,took hit rate of the weapon into consideration,allowed a second attack on the targets which has not be ruined which makes the research more practical.(4)To make the algorithm more effective,we let a group of ants search the routes at the same time,and a public tabu list was used to avoid them select the selected city so that they can work together.By changing pheromone update and selection strategy,reduced the influence of the initial generations route search.What's more,added more randomness in the selection,enhance the ability of ants to explore the unknown path.In this paper,the database Solomon eil30 has been tested,the feasibility and effectiveness of the improved ant colony algorithm in the UAVs operational planning problem have been verified by compared with the conclusion in the literatures.The improved algorithm can ensure the shortest route in UAVs route planning,and the task assignment is more reasonable and meet the needs of the combat.