Multiobjective evolutionary algorithms (MOEA) are an effective tool for solving search and optimization problems containing several incommensurable and possibly conflicting objectives. Unfortunately, many MOEAs face difficulties in solving problems when the number of objectives increases. In this paper, we investigate the efficacy of spatially structured MOEAs for scalable multiobjective problems. The algorithm is an extension of the standard cellular evolutionary algorithm, where the population is mapped to nodes of alternative complex networks. A selection regime based on a non-dominance rating and a crowding mechanism guides the evolutionary trajectory and an ε-dominance external archive is used to maintain a spread of solutions across the Pareto-optimal front. An important outcome of this work is the classification of the network models based on their impact on convergence speed and solution quality as the number of objectives increases for a given problem.