The increasing demand for cellular network capacity can be mitigated through the installation of nomadic eNodeB, which serve a temporal increase of traffic volume in specific area. When nomadic cells are deployed, the transmission power of neighbor base stations needs to be optimized to limit the inter-cell interferences. We analyze the problem of neighborhood selection for the optimization, to define what part of the networks needs to be reconfigured when new base station is added. We evaluate the iterative approach, with increasing range of neighboring cells being reconfigured and propose a novel, sampling based local TX power reconfiguration method, which is evaluated by a numerical model in both regular (honeycomb) topology and in realistic topology reflecting locations of cells in a city. The analysis confirms that the proposed algorithm allows to select very few neighboring cells which need to be reconfigured (in majority of the cases less than 10 cells) and achieve similar efficacy as global optimization, with total network throughput different by less than 1% comparing to the global optimization.