Abstract:

Aiming at the cooperative task allocation problem of UAV swarm in target recognition scenario,an optimization model with recognition cost and recognition benefit as the goal is established,and a multi-objective differential evolution algorithm based on decomposition is designed to solve the model.First,an elite initialization method is proposed,and the initial solution is screened to improve the quality of the solution set on the basis of ensuring the uniform distribution of the obtained nondominated solution.Second,the multi-objective differential evolution operator under integer encoding is constructed based on the model characteristics to improve the convergence speed of the algorithm.Finally,a tabul search strategy with restrictions is designed,so that the algorithm has the ability to jump out of the local optimal.The algorithm provides a set of nondominated solution sets for the solution of the problem,so that a more reasonable optimal solution can be selected according to actual needs.After obtaining the allocation scheme by the above method,the task reallocation strategy is designed based on the auction algorithm,and the allocation scheme is further adjusted to cope with the unexpected situation of UAV damage.On the one hand,simulation experiments verify the effectiveness of the proposed algorithm in solving small,medium and large-scale task allocation problems,and on the other hand,compared with other algorithms,the nondominated set obtained by the proposed algorithm has a higher quality,which can consume less recognition cost and obtain higher recognition revenue,indicating that the proposed algorithm has certain advantages.

Key words: task allocation, unmanned aerial vehicles, multi-objective optimization, evolutionary algorithms, tabu search