A coordinated virtual impedance control strategy for DGs units was presented to overcome the mismatched line impedance and avoid inaccurate power sharing and circulating current [28]. Both virtual resistance and virtual inductance were simultaneously tuned to compensate the mismatched line impedance among DGs. The MG system stability was enhanced by increasing damping for the whole system. A generalized droop control (GDC) was proposed for a grid-supporting inverter based on a comparison between traditional droop control and virtual synchronous generator (VSG) control [30]. The proposed GDC can achieve satisfactory control performance and provide virtual inertia and damping properties in autonomous mode. The output active power of an inverter can follow the changing references quickly and accurately without large overshoot or oscillation in the grid-connected mode. The controller parameters and transient gains were designed and tuned using the trial-and-error method [27]. However, this method has significant drawbacks such as being time-consuming and failing to obtain the optimal settings [30]. Additionally, in large MGs, fixed-gain controllers cannot certainly adapt to disturbancesand load changes especially with parameters variation [31]. The proper selection of both MG and CPL parameters is essential to improve the power quality and system performance against load switching and different disturbances [27]. To overcome power system problems successfully, computational intelligence techniques such artificial neural networks, genetic algorithm (GA), fuzzy logic (FL), and particle swarm optimization (PSO) have been recently introduced [3; 12; 32; 33; 34; 35; 36]. These techniques are used to improve the MG transient performance [3]. However, their applications have some disadvantages [5]. A combined sizing and energy management methodology was formulated as a leader-follower problem [32]. The leader problem was focusing on sizing and aiming at selecting the optimal size for the MG components using a genetic algorithm. A low-complexity FL controller was designed and embedded in an energy management system for a residential grid-connected MG including RESs and storage capability [33]. The system assumes that neither the renewable generation nor the load demand is controllable. The main goal of the design was to minimize the grid power profile fluctuations while keeping the battery state of charge within secure limits [34]. Compared with other optimization techniques, especially GA, PSO has several advantages such as robustness, computational efficiency, simplicity, easy implementation, effective capability memory, and greater efficiency [3; 12; 35; 36]. Additionally, using PSO can avoid some GA disadvantages such as converging toward the local solution rather than the global solution, especially with improperly defined objective function, and difficulties with the dynamic datasets [35]. Furthermore, it is worth mentioning that the best results are obtained using the PSO technique [36]. In this paper, MG stability with high-level penetrations of CPLs is analyzed and investigated. An autonomous MG including CPLs is modeled and presented. PLL is employed to synchronize the involved CPLs with the MG and track the MG frequency. An optimal design of the power-sharing controller, CPLs controllers, inverters controllers, and PLL gains is performed. The control problem is designed based on minimizing a weighted objective function to limit the error in the DC voltage and the measured active power. The DGs, CPLs, and PLL controllers are coordinately tuned to improve the MG dynamic stability. Additionally, the dynamic stability of the autonomous MG is examined. Different disturbances are applied to verify and assess the impact of the optimal power-sharing parameters on the MG stability with high-level penetrations of CPLs.