Evolutionary group theoretic tabu search approach to task allocation of autonomous unmanned aerial vehicles

This paper addresses the problem of task allocation problem for multiple autonomous unmanned aerial vehicles (UAVs). Group theory is used to interpret the search space of task allocation of UAVs and search neighborhood is generated based on post multiply algebra in symmetrical group. In order to find the optimal solution to the task allocation problem, we adopt a metaheuristic hybrid. Evolutionary computation (EC) gives appropriate initial value and group theoretic tabu search (GTTS) helps to fine a better solution. Our algorithm incorporates domain-specific knowledge and takes into account different kinds of mission constraints. Simulation results validate the feasibility of our algorithm.