## 1 Introduction
The Kriging model is a stochastic model and, due to its good approximation ability to nonlinear functions with stochastic properties [12], it is one of the most representative and potential surrogate modeling methods in the field of surrogate-assisted evolutionary algorithms [13; 14]. Surrogate-assisted evolutionary algorithms guide the search process of the algorithm by using existing solutions that have been evaluated with real functionsto build surrogate models to predict the performance of the unevaluated ones. The use of surrogate models allows the algorithm to explore the decision space more fully within the constraints of a limited number of real evaluations and greatly reduces the amount of computation due to the computationally inexpensive nature of surrogate models. SA-MOEA needs to find the optimal solution under the limitation of only a few evaluation times. Therefore, the strategy of the evolutionary algorithm needs to be improved to adapt to SA-MOEA [15]. The mainstream improvement directions can be divided into three types: offspring generation, surrogate model management, and fill sampling criteria. In terms of offspring generation, KTA2 [16] uses an archived population to generate multiple sets of candidate solutions to ensure the diversity and convergence of the population. In order to meet the preference requirements within a limited number of evaluations, K-RVEA [17] uses the angle penalty distance to balance the diversity and convergence of solutions in decision space and dynamically adjust the reference vector distribution simultaneously. In terms of surrogate model management. In order to improve the prediction accuracy of the surrogate model, MOEA/D-EGO [18] adopts the idea based on decomposition and uses the MOEA/D algorithm to decompose the multi-objective problem into multiple single-objective problems and establishes a surrogate model separately for each single-objective problem. K-RVEA utilized the uncertain information of the Kriging model to design the training data selection strategy for the Kriging model in order to improve the performance of Kriging. In terms of the fill sampling criterion, CSEA [8] uses the surrogate model to predict the dominant relationship between the candidate solution and the reference solution and selects the dominant solution for real function evaluation. ABSAEA [19] uses two fill sampling criteria; one of them selects the non-inferior solution to enhance the convergence of the population, and the other selects the solution with the maximum uncertainty to enhance the diversity of the population. For ECMOPs, another problem that SAMOEA needs to solve is how to optimize the objectives under the premise of meeting the constraint conditions [20]. For SA-MOEA, predicting both the constraint values and the objective values simultaneously often leads to a large prediction error of the surrogate model [21], especially when there is an overlap between the constrained Pareto front and the unconstrained Pareto front. However, not predicting the constraint values often leads to the algorithm being unable to find the feasible region and making it difficult to search for effective feasible solutions. Therefore, SA-MOEA needs to use appropriate constraint handling techniques to balance the optimization of objective values and the satisfaction of constraint conditions. In ECMOPs, the feasible domain of the problem may be extremely small, and then the algorithm needs to accelerate the convergence to ensure that the feasible domain can be found with limited computational resources; however, existing algorithms such as KTS [22] that only consider the correlation between the constraints and the objective values are unable to accelerate the convergence in this case. On the other hand, if the algorithm focuses too much on finding the feasible domain in the early stage, the algorithm may fall into localized convergence and fail to cross the feasible domain to converge to the CPF.