Multi-aircraft task allocation(MATA)plays a vital role in improving mission efficiency under dynamic conditions.This paper proposes a novel coevolutionary genetic programming(Co GP)framework that automatically designs...Multi-aircraft task allocation(MATA)plays a vital role in improving mission efficiency under dynamic conditions.This paper proposes a novel coevolutionary genetic programming(Co GP)framework that automatically designs high-performance reactive heuristics for dynamic MATA problems.Unlike conventional single-tree genetic programming(GP)methods,Co GP jointly develops two interacting populations,i.e.,task prioritizing heuristics and aircraft selection heuristics,to explicitly model the coupling between these two interdependent decision phases.A comprehensive terminal set is constructed to represent the dynamic states of aircraft and tasks,whereas a lowlevel heuristic template translates developed trees into executable allocation strategies.Extensive experiments on public benchmark instances simulating post-disaster emergency delivery demonstrate that Co GP achieves superior performance compared with state-of-the-art GP and heuristic methods,exhibiting strong adaptability,scalability,and real-time responsiveness in complex and dynamic rescue environments.展开更多
基金Project supported by the National Natural Science Foundation of China(No.U2333218)。
文摘Multi-aircraft task allocation(MATA)plays a vital role in improving mission efficiency under dynamic conditions.This paper proposes a novel coevolutionary genetic programming(Co GP)framework that automatically designs high-performance reactive heuristics for dynamic MATA problems.Unlike conventional single-tree genetic programming(GP)methods,Co GP jointly develops two interacting populations,i.e.,task prioritizing heuristics and aircraft selection heuristics,to explicitly model the coupling between these two interdependent decision phases.A comprehensive terminal set is constructed to represent the dynamic states of aircraft and tasks,whereas a lowlevel heuristic template translates developed trees into executable allocation strategies.Extensive experiments on public benchmark instances simulating post-disaster emergency delivery demonstrate that Co GP achieves superior performance compared with state-of-the-art GP and heuristic methods,exhibiting strong adaptability,scalability,and real-time responsiveness in complex and dynamic rescue environments.
