Bidirectional Multiscale Efficient Dilated Convolutional Recurrent Neural Network Improved by Swarm Intelligence Optimization

In recent years, bidirectional convolutional recurrent neural networks (RNNs) have made significant breakthroughs in addressing a wide range of challenging problems related to time series and prediction applications. However, the performance of the models is highly dependent on the hyperparameters chosen. Hence, we propose an automatic method for hyperparameter optimization and apply a bidirectional convolutional RNN based on the improved swarm intelligence optimization (sparrow search) to solve regression prediction problems. Specifically, a parallel multiscale dilated convolution (PMDC) module was designed to capture both local and global spatial correlations. This method utilizes convolution with different dilation rates to expand the receptive field without increasing the complexity of the model. Meanwhile, it integrates parallel multiscale structures to extract features at different scales and enhance the model's understanding of the input data. Then, the bidirectional gated recurrent units (BGRUs) learn temporal information from the convolutional features. To address the limitations of empirical hyperparameter selection, such as slow training and low efficiency, a novel PMDC-BGRU model integrated with a pretrained sparrow search algorithm (SSA) was proposed for hyperparameter optimization. Finally, experiments on multiple datasets verified the superiority of the algorithm and explained the flexibility of intelligent optimization algorithms in solving model parameter optimization.