## 3 Potential of Intelligent Algorithms for Hydrodynamic
### Comparison Summary  
As illustrated in Table 2, intelligent algorithms have demonstrated significant potential in hydrodynamic noise optimization, to effectively solve high-dimensional, nonlinear, and complex constraint problems that are difficult to handle using traditional methods. As data-driven algorithms, deep learning (DL), machine learning (ML), and reinforcement learning (RL) make use of a large amount of data and high-performance computational resources to automatically learn features and patterns, construct relational models between data, and continuously optimize the control strategy through simulating the structure of neural networks in the human brain, handcrafted feature engineering, and statistical inference, as well as using trial-and-error mechanisms and reward feedback. These algorithms perform well in pattern recognition, data prediction, noise prediction, and noise source identification in fluid noise, which can help to achieve high-precision fluid noise modelling, improve prediction efficiency and applicability, and also provide new solutions for the optimal regulation of complex dynamic systems.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm Type** & **Advantage** & **Application Example** & **Technical Difficulties** & **Applicable Scenarios** \\ \hline \multirow{4}{*}{Deep Learning (DL)} & Strong data processing, & \multirow{4}{*}{Noise field prediction} & High data requirements & \multirow{4}{*}{Pattern recognition, feature extraction} \\  & pattern recognition & & and noise source & & \\  & capability, suitable for & & identification & costs & \\  & high-dimensional data & & & \\  & processing & & & & \\ \hline \multirow{4}{*}{Machine Learning (ML)} & Automatic learning & \multirow{4}{*}{Innovative exploration} & Dependent on the & \multirow{4}{*}{Real-time noise} \\  & and prediction from & of noise prediction, & quality and quantity of & \\ \cline{1-1}  & data for noise & & noise source & \\ \cline{1-1}  & prediction and noise & identification and & & \\ \cline{1-1}  & source identification & hybrid methods & capability needs to be & \\ \hline \multirow{4}{*}{Reinforcement Learning (RL)} & Based on & \multirow{4}{*}{Noise control, flow} & \multirow{4}{*}{Requires large amounts} & \multirow{4}{*}{Noise prediction and} \\  & trial-and-error & & & of data and high \\ \cline{1-1}  & mechanisms and & & & \\ \cline{1-1}  & reward feedback for & & & \\ \cline{1-1}  & optimal regulation of & & intelligent regulation & computing resources & \\ \cline{1-1}  & complex dynamic & & & \\ \cline{1-1}  & systems & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of intelligent algorithms.  
In addition, heuristic optimization algorithms such as the genetic algorithm (GA), ant colony algorithm (ACO), and particle swarm optimization (PSO) provide diversified and adaptive solution strategies by simulating biological behavior and group intelligence in nature [92]. These algorithms demonstrate powerful global search and parallel computing capabilities in scenarios such as multivariate shape optimization, material property optimization, path planning optimization, and geometric structure optimization, which allow the population to be optimized iteratively and gradually converge to the best solution. Although these algorithms have certain technical difficulties in parameter selection, convergence speed, and computational complexity, their flexibility and adaptability in fluid noise optimization problems give them wide application potential. By combining with other intelligent algorithms, these heuristic optimization algorithms are able to cope with nonlinear and multi-objective optimization requirements and enhance the source optimization and control strategies for fluid noise, thus enhancing the flexibility and efficiency of the optimization solution.