## 3 Potential of Intelligent Algorithms for Hydrodynamic
### Heuristic Optimization Algorithms  
(1) Genetic Algorithm (GA)  
The genetic algorithm is an optimization algorithm based on natural selection and genetic mechanisms, which belongs to the category of evolutionary algorithms. Underlying [PERSON]'s principle of natural selection, it emulates the processes of selection, cross-over, and variation in biological evolution. The process of evolution is characterized by the gradual development of individuals with high fitness, ultimately leading to the approach of a global optimal solution to a problem [82]. The algorithm's core strength lies in its advanced global search functionality. It has demonstrated proficiency in addressing optimization problems characterized by intricate solution spaces or nonlinear relationships [83]. Genetic algorithms augment the diversity and adaptability of the solution through a series of genetic operations, ensuring that the population is perpetually optimized in each iteration. This process ultimately leads to the gradual convergence of the population to an optimal solution. Genetic algorithms have been shown to have significant advantages in the field of multi-objective optimization, a domain that lends itself to complex design needs, such as multivariate shape optimization and material property tuning in hydrodynamic noise. For instance, [PERSON] [84] developed a generalized WEG wing design and optimization method based on a genetic algorithm, which includes three steps: parametric design by computational fluid dynamics (CFD), calculation of fluid properties by wing design software XFOIL 7.0, and iterative optimization by the genetic algorithm. The result of this methodology was a folded SJZZWGN airfoil design with low drag and noise that met the mechanical performance requirements. This finding demonstrates the efficacy of the genetic algorithm in addressing complex non-convex optimization problems, ensuring the quality of solutions and enhancing computational efficiency through global search when compared to traditional optimization methods. Ant Colony Optimization (ACO)  
The ant colony algorithm (ACA) is a heuristic optimization algorithm that emulates ant behavior in search of food. It is a member of the swarm intelligence algorithm family. The core of the ACA is to simulate the release of pheromones by ants during the foraging process. The alteration in pheromone concentration is strategically employed to direct the movement of other ants, thereby progressively refining the search trajectory. This process ultimately leads to the identification of the global optimal solution within the complex solution space. This pheromone mechanism renders the ant colony algorithm especially well-suited for the resolution of combinatorial optimization problems, including the traveling salesman problem, graph coloring, and scheduling problems [85; 86; 87]. The ant colony algorithm (ACO) is distinguished by its capacity for real-time dynamic adjustment, rendering it particularly well-suited for fluid noise control applications. It has been demonstrated to be especially adept at optimizing the layout of noise sources and facilitating real-time updates to control strategies. This, in turn, enhances the system's responsiveness and accuracy. Moreover, ACO's capacity to interface with other algorithms leads to enhanced solution quality and computational efficiency. For instance, [PERSON]'s GNS [88] integrates a genetic algorithm with an ant colony algorithm, resulting in a hybrid optimization method based on meshless computational fluid dynamics (M-HYBRID). This approach directs the design trajectory towards the reference point, leading to a substantial enhancement in the convergence speed of the algorithm and the effective realization of the objectives of multi-objective shape optimization. Notwithstanding the ant colony algorithm's challenges with convergence speed and parameter sensitivity, its flexibility and adaptability offer a vast potential for applications in complex fluid noise optimization problems.