Optimizing software engineering English translation using an enhanced Grey Wolf Optimization with self-attention and Bi-LSTM model.

Machine translation plays a crucial role in bridging language gaps, especially in specialized domains such as software engineering. Traditional neural machine translation models, including Transformer and LSTM-based models, have shown significant progress in Chinese-to-English translation tasks. However, these models often face challenges in optimizing hyperparameters dynamically and handling diverse textual domains, leading to suboptimal translation accuracy and efficiency. To address these limitations, this study proposes an enhanced translation model, Adaptive Grey Wolf Optimization with Self-Attention and LSTM (AGWO-SALSTM). The proposed model integrates an adaptive Grey Wolf Optimization (AGWO) algorithm to dynamically fine-tune hyperparameters, optimizing learning rates, attention weights, and network configurations. The combination of self-attention and bidirectional LSTM enhances contextual understanding and sequential processing, leading to improved translation accuracy. The proposed AGWO-SALSTM is validated against three baseline models: Transformer, LSTM-Seq2Seq, and MT5, across four well-established datasets: PARACRAWL, WMT, UM-Corpus, and OPUS. Experimental results demonstrate that AGWO-SALSTM consistently outperforms the baseline models in terms of translation accuracy and efficiency. Specifically, the proposed model achieves an average translation accuracy of 95.56% with the highest accuracy recorded across all datasets, outperforming the closest competitor, MT5, which achieves 90.94%. Additionally, AGWO-SALSTM requires fewer iterations to converge to a stable state, with an average of 16-20 iterations, compared to the Transformer model, which requires up to 57 iterations.