###### Abstract
[8] proposed the hybrid automatic and coordinated search method and applied it to cascade reservoirs, which improved the operational efficiency of the cascade reservoirs. [PERSON] et al. [9] established the MOOM with the goal of maximizing users' satisfaction and economic benefits. For the optimization solution, there are two types of existing optimization algorithms. Among them, traditional optimization algorithms have strong local search ability, resulting in a local optimum, so their application scope is greatly limited, including linear programming [10; 11; 12], nonlinear programming [13; 14; 15], dynamic programming [16; 17; 18; 19; 20; 21], etc. Intelligent optimization algorithms are constructed by intuition or experience, including the genetic algorithm [22; 23; 24], the ant colony algorithm [25; 26; 27; 28], the artificial neural network algorithm [29; 30], and some new algorithms, such as the artificial immune algorithm [31], the differential evolution algorithm [32], the whale optimization algorithm [33], etc. These methods have strong robustness and are suitable for solving reservoir operations. Due to the complexity of the MOODPR problem and the limitation of constraints, there is no universal solution algorithm. The scheme evaluation is a decision-making problem. To coordinate the contradiction among objectives and obtain a relatively optimal and balanced operation scheme, decision makers need to conduct multi-attribute decision making, which is an optimal decision based on a limited number of alternatives. TOPSIS [34] compares the distance closeness to sort the schemes. The lack of consideration of the correlation among attribute indexes may lead to problems such as insignificant decision results and reverse order. The set pair analysis (SPA) [35] can judge the quality according to the identical-different-contrary relations between each scheme and the optimal scheme. However, the uncertainty of the difference coefficient of this method has decision risk to a certain extent. The fuzzy set pair analysis (FSPA) [36] is based on the idea of SPA, attaches importance to relativity and fuzziness in information processing, and describes the identical-different-contrary relations between evaluation samples and evaluation grades with the fuzzy connection number, which is an effective method to analyze uncertain multi-objective decision-making problems. The grey correlation analysis (GCA) [37; 38; 39] can judge the quality of the scheme from the geometric shape of alternative schemes and optimal schemes. This method ignores the correlation among attribute indexes, and there is information loss. The grey target model (GTM) [40; 41] can judge the quality of the scheme from the distance between each scheme and the optimal scheme (target center). The method has a single target center, and there may be a problem that the decision result is not significant. The entropy weight method (EWM) [42] can judge the quality of the scheme from the balanced adjacent degree of each scheme and the optimal scheme, which makes up for the shortcomings of GCA to a certain extent. The above methods should be reasonably determined according to the specific decision information in practical applications. It can be seen that the existing research has made continuous exploration and achieved meaningful results in optimization solutions and scheme evaluation of reservoir groups. However, the existing operation schemes are difficult to implement in practical applications and it is challenging to coordinate the relationship between the ecological objective and the profit objective. The main reasons are as follows: (1) The essence of optimal operation of a reservoir group is a multiple-stage, multiple-objective, and multiple-decision attributes complex decision problem. The ultimate goal is to provide a reasonable and feasible operation scheme for decision makers. (2) Most existing multi-attribute decision making methods have large information loss and insufficient data mining.