Evolutionary Tabu Search in Task Allocation of Unmanned Aerial Vehicles

This paper addresses the problem of task allocation problem for a fleet of unmanned aerial vehicles (UAVs). An evolutionary tabu search (TS) algorithm is proposed to search the optimal solution to the task allocation problem. In this algorithm, TS serves as the mutation operator in evolutionary algorithm. Evolutionary computation (EC) gives appropriate initial value and TS helps to find a better solution. In order to meet the requirements of task reallocation in dynamic environment, a partially regroup strategy based on K-mean clustering is employed to find the new solutions in real time while keeping the optimality of results. Our algorithm incorporates domain-specific knowledge and takes into account different kinds of mission constraints. Simulation results validate the feasibility and efficiency of our algorithm