Recent studies have shown difficulties in balancing convergence and diversity for many-objective optimization problems with various types of Pareto fronts. This paper proposes an adaptive reference vector based evolutionary algorithm for many-objective optimization, termed as ARVEA. The ARVEA develops a reference vector adaptation method, which can adapt different types of Pareto fronts by adjusting the distribution of reference vectors. Besides, this algorithm adopts Pareto dominance as the first selection criterion, and the achievement scalarizing function (ASF) is introduced as the secondary selection criterion. The empirical results demonstrate that the proposed ARVEA has good performance for solving problems with various types of Pareto fronts, surpassing several state-of-the-art evolutionary algorithms designed for many-objective optimization.