Based on the above, the current work was motivated by several key points of importance:  
* Recently, the marine industry has adopted renewable energy sources, which makes it crucial to optimize SMGs. * An emerging area of research concerns the problem of the LFC of ship microgrids, which becomes particularly challenging due to the high fluctuating propulsion loads and low inertia that characterize the SMGs. * The previous research on LFC in shipboard microgrids overlooked the time delay in communication lines. This gap highlights the need for the further development of improved frequency control techniques. * Studies carried out in this field investigate and utilize metaheuristic algorithms to determine optimal controller design parameters for microgrids on ships. Metaheuristics methods have gained popularity over traditional methods in solving optimization problems due to their ease of use and the reliability of their outcomes. In recent times, swarm-based bio-inspired algorithms have been shown to be highly effective in solving a diverse array of optimization problems [32]. This paper utilized a recently developed swarm-based optimization strategy, which is called the \"jellyfish search optimizer\" (JSO). This algorithm is known for its high rate of convergence and its ability to effectively tune the controller parameters [33]. The JSO has been already used to solve a variety of problems [32] related to power system and energy generation, civil engineering, communication and networking, etc. However, it has not been applied to LFC problems until recently. In this study, the JSO was used in order to optimally tune three different controller topologies, i.e., a PID with filter (PIDF), an FOPID, and a 2 DOF-PIDF (2-degree-of-freedom PIDF) one, for the LFC problem of a multi-source ship microgrid. Furthermore, the effectiveness of the suggested approach was assessed by comparing its performance with other commonly used bio-inspired optimization methods such as ant-lion optimization (ALO) [34], the grey wolf optimizer (GWO) [35], the GOA [36], the Harris hawks optimization (HHO) [37], and the whale optimization algorithm (WOA) [38], providing a comprehensive analysis of the controller's performance. The selected algorithms share the characteristic of being parameter-free. This means that they do not have parameters that are initialized. The key contributions of this work can be summarized as follows:  
* A recently proposed shipboard microgrid system was further examined and analyzed for frequency regulation studies. This extends the scope of previous research in the field of LFC in SMGs, because the study considered the time delay between the sensor and the controller;
* The performance of different metaheuristics optimization algorithms, including ALO, the GWO, the GOA, HHO, the WOA, and the proposed JSO, was assessed. The evaluation offers insights into which algorithm is most suitable for this application;
* The performance of various controllers, including PIDF, FOPID, and 2 DOF-PIDF, was evaluated. The system dynamics were analyzed under changing solar, wave, and load conditions in the shipboard microgrid. This analysis provides a comprehensive understanding of the system dynamics and highlights the effectiveness of the controllers in regulating frequency;
* The sensitivity of the proposed JSO-tuned controller to variations in system parameters was examined. By analyzing the sensitivity, the research provides information on the controller's resilience and identified how certain parameters influence its effectiveness. The rest of this paper is structured as follows: In Section 2, the modeling aspects of the proposed frequency response model for the maritime microgrid are outlined. The design of the controllers and the performance index that are used are described in Section 3. Section 4 delves into the jellyfish search optimizer (JSO) and its application in the system.