Routing　optimization,　as　a　significant　part　of　Traffic　Engineering　(TE),　plays　an　important　role　in　balancing　network　traffic　and　improving　quality　of　service.　With　the　application　of　Machine　Learning　(ML)　in　various　fields,　many　neural　network-based　routing　optimization　solutions　have　been　proposed.　However,　most　existing　ML-based　methods　need　to　retrain　the　model　when　confronted　with　a　network　unseen　during　training,　which　incurs　significant　time　overhead　and　response　delay.　To　improve　the　generalization　ability　of　the　routing　model,　in　this　paper,　we　innovatively　propose　a　routing　optimization　method　GROM　which　combines　Deep　Reinforcement　Learning　(DRL)　and　Graph　Neural　Networks　(GNN),　to　directly　generate　routing　policies　under　different　and　unseen　network　topologies　without　retraining.　Specifically,　for　handling　different　network　topologies,　we　transform　the　traffic-splitting　ratio　into　element-level　output　of　GNN　model.　To　make　the　DRL　agent　easier　to　converge　and　well　generalize　to　unseen　topologies,　we　discretize　the　huge　continuous　traffic-splitting　action　space.　Extensive　simulation　results　on　five　real-world　network　topologies　demonstrate　that　GROM　can　rapidly　generate　routing　policies　under　different　network　topologies　and　has　superior　generalization　ability.　©　2024　Elsevier　Ltd