Large-scale global optimization problems (LSGO) is an important research topic in the field of evolutionary computation. Recently, to solve the LSGO, many approaches with excellent performance have been proposed, where the multi-space evolutionary search algorithm (MSES) is one of them. In MSES, the quantity of transfer solutions across the simplified and original problem spaces is fixed. However, the quantity of correct transfer solutions required in the target space is different for problems with distinct characteristics or distinct evolutionary stages of the same problem. Therefore, it is not an effective strategy to fix the quantity of transfer solutions. In this paper, we aim to dynamically provide a more appropriate quantity of transfer solutions through an adaptive strategy to effectively improve the capability of MSES. An advanced multi-space evolutionary search algorithm is proposed for LSGO. In particular, a new transfer strategy is proposed to enhance the positive transfer capability, which improves the capability of the algorithm effectively. In addition, this paper enhances the adaptability of the AMSES to different problems by selecting different solvers for the problem space. To assess the effectiveness of the proposed approach, the empirical experiments are performed on the CEC2013 large-scale benchmark problems.