###### Abstract
These methods can quickly solve small-scale optimization problems, but are not ideal for large-scale optimization problems. With the development of economy and society, the degree of exploitation and utilization of water resources is getting higher and higher, and the scale of water resources regulation and storage projects is getting bigger and bigger, which puts forward higher requirements for reservoir optimal operation. Modern intelligent optimization algorithms are widely used in reservoir optimal operation, such as genetic algorithm (GA) [19, 20], particle swarm optimization (PSO) [21, 22], artificial neural network (ANN) [23], simulated annealing algorithm (SA) [24] and ant colony optimization (ACO) [25, 26]. In addition, new swarm intelligence optimization algorithms continue to emerge, such as wolf pack algorithm (WPA) [27], differential evolution algorithm (DEA) [28, 29] and artificial bee colony algorithm (ABCA) [30]. In view of the flexibility of intelligent algorithms to solve optimization problems, many researchers have combined different intelligent algorithms and achieved good results in reservoir operation [31, 32, 33]. It can be seen that the current research on the optimization solution method has been relatively mature, and due to the complexity of the MODRO problem, there is no universal solution method, and each method has its applicable conditions. After solving by multi-objective optimization algorithm, it is no longer a single optimal solution, but a series of alternative schemes. For decision makers, it is necessary to make a quick judgment on the current operation situation and select the decision-making scheme that is most in line with the actual reservoir operation. Scheme selection belongs to the decision-making problem. Currently, the most widely used methods are entropy weight method [34], fuzzy set theory [35], TOPSIS decision-making method [36], vague set theory [37], interval-valued [38] and grey relational decision-making [39]. All these methods need to normalize the alternative set to obtain the initial weight coefficients or membership degrees of evaluation indexes. Considering the subjective preferences of decision makers, the objective weights or membership degrees are corrected and processed. In the decision-making process, decision makers are often affected by subjective factors, of which it is difficult to make scientific judgments. To solve this problem, [PERSON] et al. [40] proposed an approach of successive elimination of alternative schemes based on the \(k\)-order and \(p\)-degree of efficiency (SEACODE). Compared with the traditional methods, SEABODE method can directly select the scheme from the decision matrix and gradually eliminate the inferior scheme. There is no need to standardize the decision matrix or calculate the attribute weight, thus reducing the influence of subjective factors. The existing studies have made significant achievements in model construction, optimization solution and scheme selection. However, when constructing the model, the degree of conflict between objectives is not quantified, and there are redundant objectives, resulting in a large number of false non-dominated solutions; Secondly, it is ignored that the essence of reservoir operation is a complex decision-making problem with multi-objectives, multi-stages and multi-attributes, which needs to be studied from the perspective of decision-making and obtain a reasonable and feasible decision-making scheme through scientific methods. Therefore, this study proposed a method of multi-objective optimization and multi-attribute decision making for reservoir operation coupling the whole process of \"Objective-Modeling-Optimization-Selection\". Taking Huangjinxia Reservoir as an example, the multi-objective optimal operation and decision-making is carried out to verify the scientificity and feasibility of the proposed method. The rest of this paper is organized as follows. Section 2 describes the general situation of the Huangjinxia Reservoir, selection of typical years and water demand.