Learning-guided coevolution multi-objective particle swarm optimization for heterogeneous UAV cooperative multi-task reallocation problem
UAV system has been widely used in military field.Due to the complex and changeable battlefield environ-ment,UAV tasks need to be reassigned after an emergency.Heterogeneous UAVs refer to multiple types of UAVs,which can accomplish multiple types of complex tasks that a single UAV can not.The heterogeneous UAV cooperative multi-task reallocation problem has complex constraints and mixed variables,and the existing multi-objective optimization algorithms can not deal with this kind of problems effectively.In order to solve the above problems efficiently,a multi-constraint heterogeneous UAVs cooperative multi-task reallocation model is constructed at first in this paper,and a learning-guided cooperative multi-objective particle swarm optimization algorithm(LeCMPSO)is proposed to solve that.In LeCMPSO,a prior knowledge based initialization strategy as well as a history information learning based particle update strategy are introduced to avoid the generation of infeasible solutions and improve the search efficiency of the algorithm.The simulation results on 4 sets of examples show that the proposed algorithm outperforms the other typical coevolutionary multi-objective optimization algorithms on diversity of solution sets,convergence,and search time.
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