Abstract: Although the research on swarm intelligence optimization algorithms inspired by animal and human behaviors has made good progress in function optimization,further exploration is needed in the research of cross-field general algorithms.This paper proposes a cross-field optimization algorithm combined with reinforcement learning and artificial hummingbird algorithm(AHA).In the guided foraging and territorial foraging processes of AHA,hummingbirds and different foraging behaviors are used as agents and actions to build the reinforcement learning architecture.By using a reinforcement learning reward mechanism,the hummingbird agent selects appropriate actions to optimize the movement range of individual hummingbirds,ultimately improving the optimization effect of the algorithm.In order to test the optimization effect and universality of the algorithm,comparative experiments are conducted on function optimization problems,flexible workshop scheduling(FWS) problem,location optimization problem of logistics center(LLC),and routing optimization problem of unmanned aerial vehicle(UAVRO) in oil factories.Experimental results demonstrate that the individuals obtained after the same number of iterations of the proposed algorithm are superior,and the solutions obtained in the corresponding field are relatively better.

Key words: Swarm intelligence optimization, Reinforcement learning, Artificial hummingbird algorithm, Cross-field optimization, Engineering applications