In practice, when using multi-objective optimization algorithms, people use reference information to search for desired solutions. However, including decision maker reference information can cause the evolutionary process to lose balance between exploration and exploitation capabilities, thereby leading to missing good solutions or being locally optimized. Recently, there have been many effective proposals to analyze trends and maintain the balance automatically. To simultaneously address decision maker desires and self-regulation capabilities, this paper proposes a method that combines decision maker information and adaptive control information applied on the DMEA-II, MOEA/D using reference points. The experimental results created a good balance in using the two reference information types in the evolutionary process.