## 3 Data Modeling Methodology
### Hybrid Differential Evolution and Gray Wolf Optimization Algorithm (DE-GWO-SVR)  
In this paper, we propose to adopt the high-dimensional mapping based on the RBF kernel function. Therefore, the value of the mapping parameter gamma will play a decisive role in mapping the data from low to high dimensions, which affects the prediction speed and accuracy of the model. At the same time, the value of the penalty coefficient \(C\) in the SVR can directly determine the generalization ability of the model, the higher the \(C\), the lower the tolerance of the model for error, and the model may fall into the overfitting state; the lower the C, the higher the tolerance of the model for error, and the model is can easily fall into the state of underfitting. Thus, the selection of \(C\) has a direct impact on the prediction accuracy of the model on the unknown DO value in this paper, and the GWO algorithm has the advantages of fast convergence and high accuracy of optimization algorithm parameters. So, this algorithm is used this paper to optimize C and gamma in SVR simultaneously. The fitness function formula is as follows:  
\[Fitness=\frac{\sum_{i=1}^{n}(y_{i}-\widehat{y_{i}})^{2}}{n}\times 100 \tag{14}\]The GWO algorithm is a single algorithm that \(\alpha\) wolf, \(\beta\) wolf, \(\delta\) wolf to guide other wolves in randomized hunts, if the \(\alpha\) wolf, \(\beta\) wolf and \(\delta\) wolf fall into local optimum during random initialization, the whole algorithm will not be able to jump out of the local optimum solution to find the global optimum solution. Therefore, this paper proposes to fuse the DE algorithm with the GWO algorithm to maintain the population diversity by generating the next generation of wolves through differential evolution after each round of iteration, so as to improve the generalization ability of the model and jump out of the local optimal solution. Figure 2 shows the co-evolution process of the DE-GWO-SVR model and the steps to realize the prediction of DO content in perch aquaculture water using this algorithm. Step 1 As shown in Figure 2, data preprocessing is first performed on the obtained water quality parameters to fill the missing values in the data and normalize the data so as to improve the convergence speed of the model in training. The dataset is divided into training, validation, and test sets. The training and validation sets are used to iterate the DE-GWO optimization algorithm. Step 2 Initialize the parent, mutant and offspring wolves with the model parameters, the position of each wolf is assigned to the parameter \(C\) and gamma value in the SVR model, the fitness function value of the parent wolf needs to be computed once before the start of the iteration, the mean square error of the model for the validation set after each iteration is selected as the fitness function value, and the top three wolves with the optimal fitness function value are set to be \(\alpha\) wolf, \(\beta\) wolf, and \(\delta\) wolf. Figure 2: Flowchart of optimization of DE-GWO-SVR model. Step 3 Set the number of population iterations, during which it is first needed to keep on iterating according to the \(\alpha\) wolf, \(\beta\) wolf and \(\delta\) wolf's position to get the position of the other wolves that should be updated next. After traversing all the wolves, the top three wolves with optimal adaptation are selected as \(\alpha\) wolf, \(\beta\) wolf, and \(\delta\) wolf next time. Then perform variation operation on each wolf in the current wolves, cross the mutated intermediate wolves with the parent wolves to generate the offspring wolves, and determine the offspring's \(\alpha\) wolf, \(\beta\) wolf, and \(\delta\) wolf before the next round of the cycle.