In this paper, we propose δ-similar elimination to induce a better distribution of non-dominated solutions and distribute more fairly selection pressure among them in order to improve the search performance of multiobjective evolutionary algorithms in combinatorial optimization problems. With the proposed method similar individuals are eliminated in the process of evolution by using the distance between individuals in objective space. We investigate four eliminating methods to verify the effects of δ-similar elimination and compare the search performance of enhanced NSGA-II by our method and by controlled elitism, which emphasizes the inclusion of lateral diversity.