# Research on Agricultural Product Price Prediction Based on Improved PSO-GA
## 1 Introduction  
As the main production area of Chinese scallions, Shandong Province has an important impact on the price fluctuations of scallions for all participants in the agricultural industry chain [1; 2; 3]. When there is an oversupply, it often leads to low prices hurting farmers and stockpiling, while when there is a shortage of supply, there is often a problem of \"scarcity of goods is expensive\" and market price inflation [4; 5; 6]. This mismatch between production and market demand leads to high price volatility [7; 8; 9]. However, in the current market environment, due to the lack of effective price forecasting technology, it is difficult for farmers to judge the scale of planting, for supply chain managers to effectively plan inventory and logistics, and for the government to formulate relevant industry policies [10; 11; 12]. The price of scallions is a very important factor in the development of the industry. Therefore, the accurate forecasting of scallion prices is important for many participants in order to understand market trends, make rational decisions, and promote the balance of supply and demand, as well as the sustainable development of the whole industry. In the field of agricultural product price forecasting, extensive research has been carried out at home and abroad. Previous methods for forecasting agricultural commodity prices can be roughly categorized into three types: traditional methods, machine learningmethods, and hybrid methods [13]. In traditional methods, commonly used forecasting models include linear regression, autoregressive integrated moving average (ARIMA) [14], smoothing (ES) [15], etc., which are widely used in agricultural price forecasting. However, these methods often exhibit problems such as unstable prediction accuracy when dealing with non-smooth, nonlinear data, such as the scallion price series [16]. With the rapid development of artificial intelligence technology, machine learning methods have gradually emerged in agricultural price prediction [17; 18]. These include multilayer perceptron (MLP) [19], Long Short-Term Memory (LSTM) [20], and gated recurrent units (GRUs) [21]. However, it is often difficult for a single model to fully capture the complexity of data when dealing with complex data [22], and it is prone to the overfitting phenomenon. In order to overcome the limitations of single models, combinatorial modeling has gradually become a popular method for agricultural price prediction [23; 24]. In the field of forecasting, combinatorial methods refer to the same techniques as the hybrid method. By integrating the prediction results of multiple single models, combinatorial modeling aims to find the best combination to solve a specific prediction problem. For the process of determining the weights of the combined model, the traditional simple weight statistics method [25; 26] has been gradually replaced by intelligent optimization algorithms, such as the Artificial Bee Colony (ABC) [27], particle swarm optimization (PSO) [28], and genetic algorithms (GAs) [29] because of its inability to effectively utilize the characteristics of the base models [30]. However, for problems such as determining the weights of a combined model, the use of only a single optimization algorithm tends to suffer from problems such as the tendency to fall into local optimal solutions. Therefore, in order to solve the above problems, this paper proposes an optimized PSO-GA weight assignment method [31], which utilizes elite learning [32], uniform variation, and arithmetic crossover methods for the optimization in the serial mixing of PSO and the GA and makes full use of the global search ability of particle swarm optimization algorithms, as well as the characteristics of population diversity in genetic algorithms, to achieve complementarity of strengths [33] in order to form optimal weights in the combined model.