The work aims to improve the performance of the bat algorithm (BA) used in solving the problem of packaging waste reverse logistics. Based on standard BA, a hybrid bat algorithm (HBA) was proposed. Firstly, a new bat expression was constructed to make BA suitable for the solution to the problem of packaging waste reverse logistics. Secondly, the speed update formula of bat was reconstructed by introducing the adaptive inertia weight. Then, particle swarm optimization algorithm (PSO) was introduced, which carried out particle swarm operation on any random bat in each iteration. Finally, simulation tests were carried out on enterprise examples and standard examples by HBA. The optimal recovery distance of enterprise was 776.63 km. HBA could get better results compared with genetic algorithm (GA), ant colony optimization algorithm (ACO) and tabu search algorithm (TS); such as, the number of standard examples of the known optimal solution was 6, the average error between the best solution and known optimal solution was 8.58%, and the minimum average running time was 4.39 s. HBA algorithm is superior to GA, ACO and TS in global optimization ability, stability and running speed.