Optimization of Analog Circuit Parameters Using Bidirectional Long Short-Term Memory Coupled with an Enhanced Whale Optimization Algorithm

The development of surrogate models based on limited data is crucial in enhancing the speed of structural analysis and design optimization. Surrogate models are highly effective in alleviating the challenges between design variables and performance evaluation. Bidirectional Long Short-Term Memory (BiLSTM) is an advanced recurrent neural network that exhibits significant advantages in processing sequential data. However, the training of BiLSTM involves the adjustment of multiple hyperparameters (such as the number of layers, the number of hidden units, and the learning rate), which complicates the training process of the model. To enhance the efficiency and accuracy of neural network model development, this study proposes an Improved Whale Optimization Algorithm-assisted BiLSTM establishment strategy (IWOA-BiLSTM). The new algorithm enhances the initial population design and population position update process of the original Whale Optimization Algorithm (WOA), thereby improving both the global search capability and local exploitation ability of the algorithm. The IWOA is employed during the training process of BiLSTM to search for optimal hyperparameters, which reduces model training time and enhances the robustness and accuracy of the model. Finally, the effectiveness of the model is tested through a parameter optimization problem of a specific analog circuit. Experimental results indicate that, compared to traditional neural network models, IWOA-BiLSTM demonstrates higher accuracy and effectiveness in the optimal parameter design of analog circuit engineering problems.