This paper proposes a weak association-based adaptive evolutionary algorithm(WAEA) on many-objective optimization by improving the previous decomposition approaches. Firstly, an association strategy has been presented based on the angle subspace, which can make a solution associated with multiple reference vectors. Then, the idea of weak association has been employed to design a bimodal scalar function which improves the capability of dealing with the complex PF problem. Moreover, through the detection of the number of solutions in the reference vector subspace, the proposed algorithm is capable of doing self-adaption to adjust the size of penalty parameters to efficiently deal with multi-type issue on many-objective optimization. Finally, the proposed WAEA is compared with eight representative many-objective based algorithms, respectively. The results show that the WAEA has the capability of gaining better balance of the Pareto optimum in convergence and diversity while dealing with high-dimensional many-objective problems.