<p>In this work, we propose a method for solving large-scale multi-objective problems based on problem transformation strategy. The key point of this method lies in how to construct the search subspace. First, the algorithm obtains a set of direction vectors in the decision space, which are combined in pairs to construct a set of subspaces. To obtain direction vectors with a uniform distribution as much as possible, we introduce the opposition-based learning strategy. Then, based on these subspaces, the original high-dimensional problem is transformed into a relatively lower-dimensional problem. A multi-objective evolutionary algorithm is used to quickly obtain a set of quasi-optimal solutions for the transformed lower-dimensional problem, and this set of solutions is further optimized in the original high-dimensional decision space. To validate its performance, the proposed algorithm is compared with six state-of-the-art large-scale multi-objective algorithms on various benchmark test problems, including one practical application. The experimental results demonstrate that the proposed algorithm shows competitive performance for dealing with large-scale multi-objective optimization problems.</p>