## 2 Related Work  
In 1952, [PERSON] first applied statistical measures to portfolio selection and proposed the MV model. In 1959, he further introduced the MPT based on the concept of diversification [23] which then became a hot topic in the past few decades. This theory offers an effective tool to obtain the minimized risk under a return target or vice versa. However, several obvious shortcomings have been exposed gradually with the application of the MPT and the MV model, such as depending solely on historical data and being prone to extreme asset allocation. These problems inspired a multitude of researchers to focus on the creative task of model improving.  
In 1991, [PERSON] and [PERSON] proposed the BL model by taking into account investor preferences from the perspective of Bayesian analysis [4]. From then, many researches have proved its advantages in volatile markets [24], which further contributes to its prosperity. For example, [PERSON] et al. [25] gave a readable mathematical description of the BL model and presented some extensions. [PERSON] and [PERSON] [26] proposed a modified BL model that incorporates positive views of the performance of hedge fund strategies to extend its application to the hedge fund market. [PERSON] [7] clarified the assumptions and formulation of the BL model and extended it to large portfolio applications using a dimension-reduction technique. [PERSON] [27] combined a factor structure to BL model and applied it to multi-asset portfolio. [PERSON] and [PERSON] [28] introduced a generalized BL model that accounts for both misspecification and bias in the equilibrium and expert models.  
As mentioned earlier, the BL model attaches importance to investor preferences. As a result, particular attention is paid to the interesting issue of how to generate investor preferences systematically and precisely. [PERSON] and [PERSON] [12] made use of random forests to generate investor views and compared the results with other portfolio optimization frameworks. [PERSON] [29] used machine learning (ML) classifiers to acquire investor views and revealed that the enhancement can improve the performance of the BL model significantly. [PERSON] et al. [30] combined GARCH modeling and support vector regression (SVR) to create a novel approach to describe views. In this approach, they forecasted asset returns by SVR with input indicators generated by GARCH and took the returns as the investor views. [PERSON] et al. [15] generated quantitative opinions by ML algorithms and demonstrated that random forest maintains the best performance.  
BL portfolio construction is on the basis of the classic return-risk optimization, which is a typical MOP. EMO algorithms may be a good choice. However, some difficulties (such as non-reproducibility, the computationally expensive diversity-preservation operator, and the lack of a theoretical guarantee of global convergence) hinder their application. Some researchers change direction to deterministic algorithms. The DIRECT algorithm is an efficient deterministic algorithm that guarantees global convergence. Motivated by its merits, the DIRECT algorithm began to be applied to multi-objective optimization. For example, [PERSON] et al. [31] combined the DIRECT algorithm and multi-objective genetic algorithm (MOGA) to create a novel method called NSDIRECT-GA. In this method, DIRECT is adopted to generate the population used in MOGA. In particular, rank and crowding distance are used to generate potentially optimal hyperrectangles in DIRECT. [PERSON] and [PERSON] [32] extended the DIRECT algorithm and presented the so-called MO-DIRECT. Moreover, they compared MO-DIRECT with other decomposition-based multi-objective techniques and demonstrated its superiority. [PERSON] et al. [33] introduced a multi-objective DIRECT based on the hypervolume indicator, in which the diversification is enhanced by selecting hyperrectangles with higher hypervolume.