This paper presents a novel approach to enhance the stability of DC microgrids (DCMs) by integrating intelligent control strategies and hybrid optimization techniques, specifically the Firefly Algorithm-Particle Swarm Optimization (FA-PSO), to tune Takagi-Sugeno Fuzzy Inference Systems (TSFIS), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Fractional Order Proportional-Integral-Derivative (FO-PID) controllers. The study addresses the challenges of integrating renewable energy sources (RES) such as solar photovoltaics and wind turbines into DCMs for fast DC charging of electric vehicles (EVs), which include low inertia, power fluctuations, and voltage instability. The hybrid FA-PSO approach optimizes power management within the DCM, ensuring faster convergence, superior accuracy, and reduced topological constraints. A comprehensive Small Signal Stability Analysis (SSSA) evaluates the impact of the hybrid optimization techniques on DC microgrid stability. A hardware prototype validates these strategies under real-world uncertainties, such as varying wind speed and solar insolation, demonstrating their effectiveness and feasibility for practical DC microgrid applications with integrated EV charging. The study also proposes a unified controller design methodology using a fractional-order PID (FO-PID) controller, which is optimized using FA-PSO to improve the performance, robustness, and stability of DC-DC boost converters. The FA-PSO algorithm is combined with fuzzy logic control algorithms, including TSFIS and ANFIS, to enhance the control performance of the DC microgrid. The results show that the proposed control strategies significantly improve the stability and efficiency of the DC microgrid, making it more adaptable to system uncertainties and nonlinearities. The study concludes that the hybrid FA-PSO method is effective for complex optimization problems, ensuring better results in fewer iterations.This paper presents a novel approach to enhance the stability of DC microgrids (DCMs) by integrating intelligent control strategies and hybrid optimization techniques, specifically the Firefly Algorithm-Particle Swarm Optimization (FA-PSO), to tune Takagi-Sugeno Fuzzy Inference Systems (TSFIS), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Fractional Order Proportional-Integral-Derivative (FO-PID) controllers. The study addresses the challenges of integrating renewable energy sources (RES) such as solar photovoltaics and wind turbines into DCMs for fast DC charging of electric vehicles (EVs), which include low inertia, power fluctuations, and voltage instability. The hybrid FA-PSO approach optimizes power management within the DCM, ensuring faster convergence, superior accuracy, and reduced topological constraints. A comprehensive Small Signal Stability Analysis (SSSA) evaluates the impact of the hybrid optimization techniques on DC microgrid stability. A hardware prototype validates these strategies under real-world uncertainties, such as varying wind speed and solar insolation, demonstrating their effectiveness and feasibility for practical DC microgrid applications with integrated EV charging. The study also proposes a unified controller design methodology using a fractional-order PID (FO-PID) controller, which is optimized using FA-PSO to improve the performance, robustness, and stability of DC-DC boost converters. The FA-PSO algorithm is combined with fuzzy logic control algorithms, including TSFIS and ANFIS, to enhance the control performance of the DC microgrid. The results show that the proposed control strategies significantly improve the stability and efficiency of the DC microgrid, making it more adaptable to system uncertainties and nonlinearities. The study concludes that the hybrid FA-PSO method is effective for complex optimization problems, ensuring better results in fewer iterations.