MOEA/D decomposes a multiobjective optimization problem into a set of single objective subproblems. When there are a few differences in difficulty of each objective function, it can obtain widely-spread and uniformly-distributed solutions. However, in real-world problems, the complexities of the objective functions are often heterogeneous. In this case, each subproblem of the MOEA/D has different difficulty so that the spread and uniformity of the population is deteriorated because the search direction in the objective space tends to be biased into the feasible region which is easily explored. To overcome this issue, an adaptive weight assignment strategy for MOEA/D is proposed in this paper. In the proposed method, the subproblems are divided into some groups and the convergence speed is estimated for each group and utilized as the metric of the difficulty of the subproblems. Moreover, the weight vectors of easy subproblem groups are modified to bias their search into the subproblem group with higher difficulty. Our proposed method is validated on the region-of-interests determination problem in brain network analysis whose objective functions have heterogeneous difficulties. The experimental results showed that our method worked better than the conventional weight assignment strategy in MOEA/D.