# A BiGRU Model Based on the DBO Algorithm for Cloud-Edge Communication Networks
## 3 Short-Term Traffic Load Prediction Based on DBO-BiGRU
### DBO-BiGRU Model
#### 3.2.2 DBO Algorithm Optimisation Object and Fitness Design  
The purpose of introducing the DBO algorithm into this paper is to optimise important hyperparameters in the BiGRU model. According to the description in Section 3.1.1, the parameters that need to be set reasonably in the BiGRU traffic prediction model designed in this paper include the width of the data observation window, the number of neurons in the hidden layer, and the dropout layer failure rate. In addition to these three key parameters, there is another hyperparameter that needs to be paid attention to and optimally adjusted during the actual model training, which is the neural network learning rate. As a key hyperparameter in neural network training, the learning rate has a decisive influence on the adjustment of the step size of the BiGRU neural network in the gradient descent process. When the learning rate is set too high, it may make it difficult for the network to converge during the training process, which may lead to the oscillation phenomenon, whereas a learning rate that is too low may cause the model to fall into a local optimal solution, leading to an abnormally slow convergence process.  
Taken together, the optimisation object of the DBO algorithm in this paper is a four-dimensional hyper-parameter combination consisting of the data observation window width of the BiGRU traffic prediction model, the number of neurons in the hidden layer, the dropout layer failure rate, and the neural network learning rate. In other words, the dimension of the optimisation problem targeted by the DBO algorithm in this paper is 4. The positional boundaries of the dung beetle population of the DBO algorithm, which is the optimisation object's optimisation range in the optimisation process, need to be set reasonably.  
In the DBO algorithm, the criterion for determining the degree of superiority or inferiority of the position of the individuals in the population is the size of the fitness value; the smaller the corresponding fitness value of an individual is, the better the position of the individual is, and vice versa. After clarifying the object of optimisation, the design of the calculation method of the fitness value can be carried out, assuming that the position of a dung beetle individual in the four-dimensional space is \([x_{1},x_{2},x_{3},x_{4}]\). \(x_{1},x_{2},x_{3},x_{4}\) are transformed by a series of linear laws set in advance as the width of the data observation window, the number of neurons in the hidden layer, the failure rate of the dropout layer, and the learning rate of the neural network, respectively, and together they are used to construct the BiGRU traffic prediction model. The model is trained with the training set data and predicted on the test set to obtain the predicted values and the true values of the test set samples. Subsequently, the fitness value of the individual dung beetle can be calculated by using Equation (13):  
\[Fit=\sqrt{\frac{1}{n}\sum\left(Y_{pred}-Y_{real}\right)^{2}}+\frac{1}{n}\sum|(Y _{pred}-Y_{real})|, \tag{13}\]  
where \(Fit\) denotes the fitness value, \(n\) denotes the number of samples in the test set, \(Y_{real}\) denotes the true value of the samples in the test set, and \(Y_{real}\) denotes the predicted value obtained by the BiGRU traffic prediction model. The higher the prediction accuracy of the BiGRU model, the smaller the fitness value will be, and the better the corresponding location of the dung beetle individual will be.