###### Abstract
Hence, the motivation for our study is to bridge this research gap by employing heuristic algorithms to optimize control parameters, ultimately improving the performance and stability of DAB converters. Genetic Algorithm (GA) is a heuristic search and optimization algorithm inspired by the principles of biological genetics in nature [15]. It is a method of finding solutions to optimization problems by simulating natural selection and genetic mechanisms [16; 17]. One of its characteristics is population-based search, which simultaneously processes multiple solutions, increasing the search's globality and diversity. GA is adaptive, adjusting the probability of an operation according to the fitness value of an individual in order to progressively optimize the quality of the solution. GA is a powerful optimization tool suitable for various problem domains, including engineering, economics, and others [18]. It has been widely applied in practice and continuously improved and extended by combining with other optimization techniques. Control systems often involve a large number of parameters, with complex interactions and nonlinear relationships between them. Pole placement and traditional tuning methods may be limited in complex problems, while GA can handle such complexity and find optimal parameter combinations. Its flexibility, diversity, and adaptability make it widely applicable in control system design and optimization [19], making it worth studying and considering. Meanwhile, although other heuristic algorithms like Particle Swarm Optimization (PSO) have also shown impressive performance in optimization problems, GA has accumulated more practical experience and success cases in dealing with the complexities of control system parameter optimization. GA exhibits flexibility, diversity, and adaptability in global searching and parameter optimization, rendering them widely applicable in control system design and optimization. Opting for other heuristic algorithms might require more practice and case validation to ascertain their performance and applicability [20]. [PERSON] and [PERSON] [21] further elaborate that in microgrid controllers, the most stable performance occurs when parameters are adjusted using GA, while the use of PSO for parameter tuning tends to yield less stability. GA achieves the best dynamic response, whereas PSO results in the least dynamic response. Additionally, employing GA for parameter adjustment provides the lowest voltage ripple. So far, research on optimizing the control parameters of the three typical DAB controllers using GA is relatively limited. This paper aims to address this research gap by focusing on the application of GA to optimize the parameters of three typical DAB controllers. A novel metaheuristic algorithm-based control approach, namely, the method of optimizing the parameters of three typical DAB controllers using GA, is proposed. The contribution of this approach stems from GA's status as a metaheuristic algorithm, which excels at finding the best combinations of control parameters within complex parameter spaces to enhance control system performance. The results clearly demonstrate a significant improvement in DAB stabilization performance following GA optimization. The remainder of this paper is organized as follows. Section 2 describes the mathematical model of the DAB, which contains nonlinear model, small signal model and transfer functions. Section 3 describes the PI control, SMC, and MPC control methods of the DAB, as well as the basic principles of the GA. Section 4 reports the results of parameter tuning of the controller by the GA, which verifies that the GA optimizes the control performance and response speed of the DAB converter, as well as the effectiveness and superiority of the proposed method through the simulation experiments.