Pigeon-inspired fuzzy multi-objective task allocation of unmanned aerial vehicles for multi-target tracking

In this paper, a pigeon-inspired fuzzy multi-objective optimization algorithm is proposed for task allocation of multiple unmanned aerial vehicles tracking multiple ground targets in urban environment. Firstly, a multi-objective integer programming of task allocation, involving minimum total flight distance, best task allocation balance and minimum completion time, is established. Secondly, fuzzy two-phase optimization based on the relaxed order of desirable satisfactory degrees is proposed to formulate mixed integer programming regarding the linguistic importance preference of objectives. Then, an adaptive pigeon-inspired algorithm combined with auction mechanism is proposed to solve the optimization model. The position of pigeon is defined as the bidding price given by unmanned aerial vehicle for target. To satisfy the constraints and avoid existence of inferior pigeons, the auction mechanism is designed to decode the pigeon position into a feasible task allocation scheme. Finally, by comparing with the conventional particle swarm optimization, simulations validate the effectiveness and efficiency of the proposed method.