The traveling salesman problem with neighborhoods (TSPN) is a generalization of TSP and can be regarded as a combination of TSP and TPP (Touring Polygons Problem). In this paper, we propose a hybrid TSPN solution named ACO-iRBA in which the TSP and TPP tasks are tackled simultaneously by ACO (Ant Colony Optimization) and iRBA, an improved version of RBA (Rubber Band Algorithm), respectively. A major feature of ACO-iRBA is that it can properly handle situations where the neighborhoods are heavily overlapped. Experiment results on benchmark problems composed of random ellipses show that ACO-iRBA can solve TSPN instances with up to 70 regions effectively and generally produce higher quality solutions than a recent heuristic method OH.
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