A Method for UAVs Detection Task Planning of Multiple Starting Points

UAV detection task planning has been significantly applied in many domains. As a famous optimizing algorithm, the ant colony algorithm (ACA) has a good performance to solve problem of this kind because of its positive feedback characteristic. The problem of the standard ACA is that only one starting point is considered when ACA is used in UAV detection task planning. In allusion to the UAV detection task planning problem of multiple starting points, a multi-start ant colony algorithm (MS-ACA) is proposed, deferent ant colonies are allocated to every UAV starting point. To lead the searching direction, the target is marked by a shared tabu table and a shared decision table. The results of experiment prove that the MS-ACA is able to find an optimized deploying and scheduling policy of UAVs, which conforms to the scenario requirement, and is with preferable adaptation.