## 1 Introduction  
In the era of rapid technological development today, the application of electromagnetic technology is increasingly widespread in numerous fields, such as communication, electronic equipment, aerospace, etc. [1]. Electromagnetic characteristic fitting, as a key link in electromagnetic technology, plays a crucial role in enhancing system performance and optimizing design. However, current electromagnetic characteristic fitting technology faces a series of challenges in practical applications. Traditional electromagnetic characteristic fitting methods, such as the finite difference method and the boundary element method, although they can provide relatively accurate results in some cases, often face problems of low computational efficiency and insufficient adaptability to complex scenarios [2]. At the same time, in the process of electromagnetic characteristic fitting, it is also necessary to deal with the influence of factors such as multipath fading and the shadowing effect [3], which further increases the difficulty and complexity of fitting. In addition, limited spectrum resources also impose higher requirements on the accuracy and efficiency of electromagnetic characteristic fitting [4]. To solve these problems, emerging technologies such as reconfigurable intelligent surfaces (RIS) have been introduced into the electromagnetic field [5]. RIS can effectively improve the transmission environment of electromagnetic signals by adjusting the phase shift and amplitude of reflective elements. However, this also brings new challenges, such as multi-objective optimization problems in the signal transmission process, including the design of the transmit beamforming weight vector and the optimization of the RIS phase shift, among others [6]. In actual engineering optimization, the importance of dynamic multi-objective function optimization problems cannot be underestimated. The difficulty lies in the need to simultaneously handle mutually restrictive or even completely opposite objective functions. The optimization result is a set of Pareto-optimal solution sets (Pareto-front); that is, the optimal solution sets of multiple objective functions. Due to the parallel computing characteristics of evolutionary algorithms, they have inherent advantages in solving multi-objective function optimization problems. [PERSON] et al. proposed a multi-objective genetic algorithm based on non-dominated sorting (NSGA-II/NSGA-II), which solved problems such as excessive resource occupation of the Pareto archive evolution strategy, the high computational complexity of the evolutionary algorithm, and the difficult control of the population selection method. The obtained Pareto optimal solutions are more accurate [7]. [PERSON] et al. proposed an improved NSGA-I algorithm [8], which optimized the population evolution selection method to a certain extent and implemented biased selection and fitting selection for the selection of the least-featured individuals and the individuals with the largest weights. For categories with fewer instances, this algorithm can alleviate the imbalance situation and has a high classification accuracy. [PERSON], [PERSON], and others combined mathematical programming and evolutionary algorithms and proposed a decomposition-based multi-objective evolutionary algorithm (MOEAD) [9]. The core lies in setting weight coefficients for each sub-objective, transforming it into a single-objective problem for optimization, and further carrying out the optimization work of the algorithm. However, traditional optimization methods are inadequate in handling high-dimensional optimization problems, with large training overheads and difficulty in meeting requirements such as massive data generation, efficient data perception, real-time processing, and high communication request arrival rates. Faced with such complex situations, the combination of intelligent optimization algorithms and neural network learning is an inevitable trend in the development of electromagnetic characteristic fitting technology.